A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet

Article (PDF Available)inHuman Molecular Genetics 19(20):4072-82 · October 2010with146 Reads
DOI: 10.1093/hmg/ddq307 · Source: PubMed
Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10(-8). When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10(-8) threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C.


A genome-wide scan for common alleles affecting
risk for autism
Richard Anney
, Lambertus Klei
, Dalila Pinto
, Regina Regan
, Judith Conroy
Tiago R. Magalhaes
, Catarina Correia
, Brett S. Abrahams
, Nuala Sykes
Alistair T. Pagnamenta
, Joana Almeida
, Elena Bacchelli
, Anthony J. Bailey
Gillian Baird
, Agatino Battaglia
, Tom Berney
, Nadia Bolshakova
, Sven Bo
Patrick F. Bolton
, Thomas Bourgeron
, Sean Brennan
, Jessica Brian
, Andrew R. Carson
Guillermo Casallo
, Jillian Casey
, Lynne Cochrane
, Christina Corsello
Emily L. Crawford
, Andrew Crossett
, Geraldine Dawson
, Maretha de Jonge
Richard Delorme
, Irene Drmic
, Eftichia Duketis
, Frederico Duque
, Annette Estes
Penny Farrar
, Bridget A. Fernandez
, Susan E. Folstein
, Eric Fombonne
Christine M. Freitag
, John Gilbert
, Christopher Gillberg
, Joseph T. Glessner
Jeremy Goldberg
, Jonathan Green
, Stephen J. Guter
, Hakon Hakonarson
Elizabeth A. Heron
, Matthew Hill
, Richard Holt
, Jennifer L. Howe
, Gillian Hughes
Vanessa Hus
, Roberta Igliozzi
, Cecilia Kim
, Sabine M. Klauck
, Alexander Kolevzon
Olena Korvatska
, Vlad Kustanovich
, Clara M. Lajonchere
, Janine A. Lamb
Magdalena Laskawiec
, Marion Leboyer
, Ann Le Couteur
, Bennett L. Leventhal
Anath C. Lionel
, Xiao-Qing Liu
, Catherine Lord
, Linda Lotspeich
, Sabata C. Lund
Elena Maestrini
, William Mahoney
, Carine Mantoulan
, Christian R. Marshall
Helen McConachie
, Christopher J. McDougle
, Jane McGrath
, William M. McMahon
Nadine M. Melhem
, Alison Merikangas
, Nancy J. Minshew
Ghazala K. Mirza
, Stanley F. Nelson
, Carolyn Noakes
Gudrun Nygren
, Guiomar Oliveira
, Katerina Papanikolaou
, Jeremy R. Parr
Barbara Parrini
, Andrew Pickles
, Joseph Piven
Annemarie Poustka
, Fritz Poustka
, Aparna Prasad
, Jiannis Ragoussis
, Katy Renshaw
Jessica Rickaby
, Wendy Roberts
, Kathryn Roeder
, Bernadette Roge
, Michael L. Rutter
Laura J. Bierut
, Jeff Salt
, Katherine Sansom
, Daisuke Sato
Ricardo Segurado
, Lili Senman
, Naisha Shah
, Val C. Sheffield
, Latha Soorya
s Sousa
Vera Stoppioni
, Christina Strawbridge
, Raffaella Tancredi
, Katherine Tansey
Bhooma Thiruvahindrapduram
, Ann P. Thompson
, Susanne Thomson
, Ana Tryfon
John Tsiantis
, Herman Van Engeland
, John B. Vincent
, Fred Volkmar
, Simon Wallace
Kai Wang
, Zhouzhi Wang
, Kirsty Wing
, Kerstin Wittemeyer
Shawn Wood
, Lonnie Zwaigenbaum
Catalina Betancur
, Joseph D. Buxbaum
, Edwin H. Cook
# The Author 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/
licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Lead AGP investigators who contributed equally to this project.
Human Molecular Genetics, 2010, Vol. 19, No. 20 40724082
Advance Access published on July 27, 2010
Hilary Coon
, Michael L. Cuccaro
, Daniel H. Geschwind
Jonathan L. Haines
, Judith Miller
, Anthony P. Monaco
, John I. Nurnberger Jr.
Andrew D. Paterson
, Margaret A. Pericak-Vance
, Gerard D. Schellenberg
Stephen W. Scherer
, James S. Sutcliffe
, Peter Szatmari
, Astrid M. Vicente
Veronica J. Vieland
, Ellen M. Wijsman
, Bernie Devlin
and Joachim Hallmayer
Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland,
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15232, USA,
The Centre for
Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of
Molecular Genetics, University of Toronto, ON M5G 1L7, Canada,
School of Medicine and Medical Science
University College, Dublin 4, Ireland,
Instituto Nacional de Saude Dr Ricardo Jorge and Instituto Gulbenkian de
encia, 1649-016 Lisbon, Portugal,
BioFIG—Center for Biodiversity, Functional & Integrative Genomics, Campus da
FCUL, C2.2.12, Campo Grande, 1749-016 Lisboa, Portugal,
Department of Neurology, University of California at Los
Angeles, School of Medicine, Los Angeles, CA 90095, USA,
Wellcome Trust Centre for Human Genetics, University
of Oxford, Oxford OX3 7BN, UK,
Hospital Pedia
trico de Coimbra, 3000 076, Coimbra, Portugal,
Department of
Biology, University of Bologna, 40126 Bologna, Italy,
Department of Psychiatry, University of Oxford, Warneford
Hospital, Head ington, Oxford OX3 7JX, UK,
Newcomen Centre, Guy’s Hospital, London SE1 9RT, UK,
Maris Institute for Child and Adolescent Neuropsychiatry, 56128 Calambrone (Pisa), Italy,
Child and Adolescent
Mental Health, University of Newcastle, Sir James Spence Institute, Newcastle Upon Tyne NE1 4LP, UK,
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University
Frankfurt, 60528 Frankfurt, Germany,
Department of Child and Adolescent Psychiatry, Institute of Psychiatry,
London SE5 8AF, UK,
Human Genetics and Cognitive Functions, Institut Pasteur, University Paris Diderot-Paris 7,
CNRS URA 2182, Fondation FondaMental, 75015 Paris, France,
Autism Resear ch Unit, The Hospital for Sick
Children and Bloorview Kids Rehabilitation, University of Toronto, Toronto, ON M5G 1Z8, Canada,
Autism and
Communicative Disorders Centre, University of Michigan, Ann Arbor, MI 48109, USA,
Department of Statistics,
Carnegie Mellon University, Pittsburgh, PA, USA,
Department of Molecular Physiology and Biophysics, Vanderbilt
Kennedy Center, and Centers for Human Genetics Research and Molecular Neuroscience, Vanderbilt University,
Nashville, TN 37232, USA,
Autism Speaks, New York, NY 10016, USA,
Department of Psychiatry, University of
North Carolina, Chapel Hill, NC 27599, USA,
Department of Child Psychiatry, University Medical Center, Utrecht
3508 GA, The Netherlands,
pital Robert Debre
, Child and Adolescent Psychiatry, 75019 Paris, France,
Department of Speech and Hearing Sciences,
Department of Medicine,
Department of Psychiatry and
Behavioral Sciences,
Department of Biostatistics and
Department of Medicine, University of Washington, Seattle,
WA 98195, USA,
Disciplines of Genetics and Medicine, Memorial University of Newfoundland, St John’s, NL A1B
3V6, Canada,
The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33101, USA,
Division of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada,
Department of Child and Adolescent
Psychiatry, Go
teborg University Go
teborg, Go
teborg S41345, Sweden,
The Center for Applied Genomics, Division
of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA,
Department of
Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON L8N 3Z5, Canada,
Department of Child Psychiatry, Booth Hall of Children’s Hospital, Blackley, Manchester M9 7AA, UK,
Institute for
Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608, USA,
of Pediatrics, Children’s Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia,
PA 19104, USA,
Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg
69120, Germany,
The Seaver Autism Center for Research and Treatment, Department of Psychiatry, Mount Sinai
School of Medicine, New York 10029, USA,
Autism Genetic Resource Exchange, Autism Speaks, Los Angeles,
CA 90036-423 4, USA,
Centre for Integrated Genomic Medical Research, University of Manchester, Manchester
Lead AGP investigators who contributed equally to this project.
To whom correspondence should be addressed at: Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara St,
Pittsburgh, PA 15213, USA. Tel: +1 4122466642; Fax: +1 4122466640; Email: devlinbj@upmc.edu
Human Molecular Genetics, 2010, Vol. 19, No. 20 4073
M13 9PT, UK,
INSERM U995, Department of Psychiatry, Groupe hospitalier Henri Mondor-Albert Chenevier,
AP-HP, University Paris 12, Fondation FondaMental, Cre
teil 94000, France,
Nathan Kline Institute for Psychiatric
Research (NKI), 140 Old Orangeburg Road, Orangeburg, NY 10962, USA,
Department of Child and Adolescent
Psychiatry, New York University and NYU Child Study Center, 550 First Avenue, New York, NY 10016, USA,
Department of Psychiatry, Division of Child and Adolescent Psychiatry and Child Development, Stanford University
School of Medicine, Stanford, CA 94304, USA,
Department of Pediatrics, McMaster University, Hamilton, ON L8N
3Z5, Canada,
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA,
Psychiatry Department, University of Utah Medical School, Salt Lake City, UT 84108, USA,
Department of
Psychiatry and
Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA,
Department of Human Genetics, University of California at Los Angeles School of Medicine, Los Angeles, CA 90095,
Centre for Addiction and Mental Health, Clarke Institute and Department of Psychiatry, University of Toronto,
Toronto, ON M5G 1X8, Canada,
University Department of Child Psychiatry, Athens University, Medical School, Agia
Sophia Children’s Hospital, 115 27 Athens, Greece,
Institutes of Neuroscience and Health and Society, Newcastle
University, Newcastle Upon Tyne NE1 7RU, UK,
Department of Medicine, School of Epidemiology and Health
Science, University of Manchester, Manchester M13 9PT, UK,
Carolina Institute for Developmental Disabilities,
University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3366, USA,
Centre d’Eudes et de Recherches en
Psychopathologie, University de Toulouse Le Mirail, Toulouse 31200, France,
Social, Genetic and Developmental
Psychiatry Centre, Institute of Psychiatry, London SE5 8AF, UK,
Department of Psychiatry, Washington University in
St Louis, School of Medicine, St Louis, MO 63130, USA,
Department of Pediatrics and Howard Hughes Medical
Institute Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA,
Neuropsichiatria Infantile,
Ospedale Santa Croce, 61032 Fano, Italy,
Child Study Centre, Yale University, New Haven, CT 06520, USA,
Department of Psychiatry, Carver College of Medicine , Iowa City, IA 52242, USA,
Department of Pediatrics,
University of Alberta, Edmonton, AL T6G 2J3, Canada,
INSERM U952 and CNRS UMR 7224 and UPMC Univ Paris
06, Paris 75005, France,
Center for Human Genetics Research, Vanderbilt University Medical Centre, Nashville, TN
37232, USA,
Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and
Battelle Center for Mathematical Medicine, The Research Institute at Nationw ide Children’s Hospital and The Ohio
State University, Columbus, OH 43205, USA
Received April 8, 2010; Revised July 2, 2010; Accepted July 16, 2010
Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk
has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation,
the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million
single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In
one of four primary association analyses, the association signal for marker rs4141463, located within
MACROD2, crossed the genome-wide association significance threshold of P < 5 3 10
. When a smaller
replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent
with the winner’s curse, its effect size in the replication sample was much smaller; and, for the combined
samples, the association signal barely fell below the P < 5 3 10
threshold. Exploratory analyses of pheno-
typic subtypes yielded no significant associations after correction for multiple testing. They did, however,
yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST 8SIA2 and TAF1C.
A portion of the genetic roots of autism trace to rare de novo
and inherited copy number variants (CNVs), many of which
hit genes that encode proteins affecting neuronal development,
especially formation of synapses (1). These findings make
sense in the context of autism, a neurodevelopmental disorder
arising in childhood that is characterized by impairments in
social communication and a pattern of repetitive behavior
and restricted interests (2,3).
Autism, the prototypical autism spectrum disorder (ASD), is
diagnosed in 1520 per 10 000 people (4). The broader
ASD category affects at least 60 in 10 000 children (5), but
may be as high as 100 in 10 000 (6). Consistent with substan-
tial heritability of ASD, risk to siblings of a proband with
autism is 510%, substantially higher than population
4074 Human Molecular Genetics, 2010, Vol. 19, No. 20
prevalence (7). A spectrum of severity is plausible due to the
distribution of milder phenotypes in relatives of probands
As yet, however, only rare de novo and inherited variants
are soundly established genetic risk factors for ASD,
and thus far these only account for a small proportion of the
total genetic risk. Autism is a possible manifestation of single-
gene disorders, such as those due to mutations in FMR1, TSC1,
TSC2, MECP2 and PTEN. Some chromosomal rearrangements
appear causal, with the most common being maternal dupli-
cation of 15q11 q13. Mutations of high penetrance for ASD
have been identified in synaptic genes, including NLGN3,
NLGN4X and SHANK3 (10,11). Rare deletion CNVs of
SHANK3 and the surrounding 22q13.33 region have also
been found in individuals with an ASD. In this regard,
genome-wide microarray studies have implicated a substantial
number of other individually rare submicroscopic CNV loci,
including hemizygous deletions and duplications of 16p11.2,
NRXN1 and PTCHD1 (1218).
These microscopic and submicroscopic CNVs are presumed
to have a major and sometimes causal impact on risk for ASD.
In contrast, common variants rarely have such an impact on
risk for any disorder, especially one like ASD that is known to
diminish reproductive success. Nonetheless, even if a
common variant has only a small impact on individual risk, its
population attributable risk could be substantial because it is
carried by many individuals. To date, studies identifying
common variants affecting ASD risk have met with limited
success. In addition to candidate-gene association studies, in
which some genes garner supporting evidence (19), genome-
wide association (GWA) studies have highlighted two ASD
risk loci: 5p14.1, between the neuronal cadherin loci CDH9
and CDH10 (20), and 5p15.2, between the semaphorin
(SEMA5A) and bitter taste receptor (TAS2R1) genes (21).
To search for additional common variation contributing to
ASD susceptibility, the AGP conducted high-resolution geno-
typing to examine .1500 families. Our principal GWA analy-
sis uses an additive model and our principal partitions of the
data split along two axes: all ancestry versus European; and
inclusive spectrum versus strict diagnostic groups. In explora-
tory analyses, we used additional phenotypic dimensions of
ASD to localize susceptibility loci.
ASD families and genotyping
The AGP Consortium, which represents more than 50 centers in
North America and Europe, collected data from 1558 ASD
families (4712 subjects) for this study (Supplementary
Material, Table S1). Both Autism Diagnostic
Interview-Revised, ADI-R (2), and Autism Diagnostic Obser-
vation Schedule, ADOS (3), were used for research diagnostic
classification. Nested research classification of subjects into
‘strict’ or ‘spectrum’ (i.e. encompasses strict) was developed
based on ADI-R and ADOS classification. Subjects with
known karyotypic abnormalities, fragile X mutations or other
genetic disorders were excluded. Genotyping was performed
by using the Illumina Human 1M-single Infinium BeadChip
array. A total of 1369 ASD families comprising 1385 ASD pro-
bands (Table 1) passed quality control (QC) filters (Supplemen-
tary Material, Table S1). Counting up to third-degree relatives
in the 1369 families, 43.6% had two or more ASD children
(multiplex), 42.4% had one affected child (simplex) and 14%
were unknown (extended family not evaluated); note,
however, that we typically genotyped only one proband per
family, as well as parents, even if the family were multiplex.
Proband distribution by gender was 84% male and 16%
female; 58.6% attained a strict research diagnosis of autism;
and, based on genetic analysis, 88% of subjects were of Euro-
pean ancestry (Supplementary Material, Fig. S1).
Genome-wide SNP association: primary analyses
A priori we planned and conducted four nonindependent GWA
analyses corresponding to data partitions along axes of diagno-
sis and ancestry: spectrum versus strict and European versus all
ancestries (Table 2, Supplementary Material, Table S1 3,
Fig. S2). Q Q plots (Supplementary Material, Fig. S3) show
that the distributions of observed test statistics are only modestly
different from their expected distributions under the null
hypothesis of no association. Largest associations arise in a
300 kb intronic region of MACROD2 for the most homogeneous
samples, strict diagnosis and European ancestry (Fig. 1 and
Table 2). The most noteworthy association occurs at
rs4141463, P ¼ 2.1 × 10
, which falls below a commonly
used GWA significance threshold of 5 × 10
We sought support for the results obtained from the primary
analyses by two approaches. First, we analyze independent
ASD families from the Autism Genetics Resource Exchange
(AGRE) database, combining AGP trios with AGRE simplex/
multiplex families to perform a ‘mega-analysis’ in 2179 families
for the four primary analyses. This ‘mega-analysis’, performed
on all markers, did not add much additional support (Table 2),
in terms of more significant association signals identified at the
discovery phase, and no new loci emerged for their association
with ASD (see Supplementary Material). For example, at
rs4141463 (in MACROD2), the estimated odds ratio changed
from 0.56 to 0.65 for the strict diagnosis (Table 2), European
ancestry, resulting in a P-value of 4.7 × 10
. An observation
merits consideration for this and related results. Although the
common allele is over-transmitted in both the original and
AGRE data sets, the differential transmission as measured by
Table 1. Number of families (number of probands) used for analysis
Group AGP Discovery AGRE Combined
Primary analysis
Spc|All 1369 (1385) 595 (1086) 1887 (2394)
Str|All 809 (812) 431 (687) 1181 (1440)
Spc|Eur 1217 (1230) 440 (783) 1603 (1959)
Str|Eur 718 (720) 311 (485) 984 (1160)
Exploratory analyses
Spc|Verbal 897 (909) 476 (702) 1314 (1552)
Spc|Non-Verbal 453 (454) 295 (375) 731 (812)
IQ . 80 561 (564)
IQ , 70 279 (281)
Spc, spectrum; Str, strict; All, all ancestries; Eur, European ancestry.
We could not derive an assessment of IQ for the AGRE data that would be
comparable to that from the AGP.
Human Molecular Genetics, 2010, Vol. 19, No. 20 4075
the odds ratio is notably smaller for the latter, a result that is con-
sistent with the winner’s curse (22). Thus, if rs4141463 truly
confers risk, the estimate from the AGRE data is a more realistic
estimate of risk.
We also combined the results from the family-based analy-
sis with allele frequencies from control data from the Study on
Addiction: Genetics and Environment (SAGE), also geno-
typed with the Illumina Human 1M-single Infinium BeadChip
Table 2. Results from primary analyses of AGP Discovery, AGRE, and SAGE data sets
Features SNPs associated in discovery set
Minor allele frequency 0.34 (G) 0.33 (G) 0.31 (A) 0.34 (C) 0.06 (G) 0.04 (A) 0.43 (G) 0.02 (A) 0.43 (A)
Group Str|Eur Spc|All Str|All Spc|Eur Spc|Eur Spc|All Spc|Eur Spc|Eur Str|Eur
AGP discovery OR
1.64 1.41 0.61 1.43 0.54 0.52 0.69 0.38 0.56
95% CI 1.351.99 1.231.61 0.510.72 1.251.64 0.410.70 0.39 0.69 0.60 0.79 0.240.58 0.47 0.67
P 4.7 × 10
3.7 × 10
2.2 × 10
4.8 × 10
1.9 × 10
1.7 × 10
1.1 × 10
1.0 × 10
2.1 × 10
AGRE OR 1.08 0.85 1.00 1.13 0.73 0.88 1.21 0.71 0.84
95% CI 0.861.37 0.730.99 0.831.22 0.951.34 0.521.02 0.41 1.88 1.03 1.43 0.441.12 0.67 1.04
P 5.2 × 10
4.6 × 10
1.0 × 10
1.8 × 10
4.1 × 10
7.1 × 10
2.9 × 10
1.0 × 10
1.3 × 10
AGP + AGRE OR 1.38 1.13 0.77 1.29 0.62 0.56 0.86 0.48 0.65
95% CI 1.191.61 1.031.25 0.680.88 1.161.44 0.500.76 0.43 0.72 0.77 0.95 0.350.66 0.57 0.75
P 8.5 × 10
2.3 × 10
2.3 × 10
2.7 × 10
2.1 × 10
4.2 × 10
9.3 × 10
7.0 × 10
4.7 × 10
AGP + SAGE OR 1.32 1.24 0.76 1.24 0.59 0.59 0.80 0.45 0.69
95% CI 1.151.52 1.121.38 0.660.87 1.111.38 0.470.75 0.46 0.76 0.73 0.89 0.310.66 0.60 0.79
P 8.8 × 10
3.8 × 10
8.5 × 10
7.5 × 10
7.5 × 10
1.4 × 10
2.6 × 10
1.7 × 10
8.1 × 10
OR 1.25 1.09 0.84 1.19 0.64 0.63 0.91 0.54 0.73
95% CI 1.111.41 1.001.18 0.760.93 1.101.30 0.530.78 0.50 0.79 0.84 0.99 0.400.73 0.66 0.82
P 2.0 × 10
4.6 × 10
1.0 × 10
5.6 × 10
2.9 × 10
5.7 × 10
2.8 × 10
2.1 × 10
3.7 × 10
Spc, spectrum; Str, strict; All, all ancestries; Eur, European ancestry.
Reporting restricted to SNPs with statistics meeting or falling below the threshold of P , 5 × 10
. See Supplementary Material for description of all SNPs with
statistics meeting or falling below the threshold of P 5 × 10
Bold emphasis denotes SNPs discussed.
Top association signal reported only; additional signal at reporting threshold for markers in strong LD include rs6079536, rs6079537, rs6079540, rs6079544,
rs6074787, rs10446030, rs6079553, rs4814324, rs6074798, rs980319 (Supplementary Material, Table S2).
Odds ratio based on the minor allele.
Figure 1. Association results, presented as the 2 log(base 10) of the P-values, for an intronic region of MACROD2 (20p12.1). The panels show the combinations
of two diagnostic levels, strict versus spectrum and any versus European ancestry of the subjects. Recombination rates were calculated using Release 22 of the
HapMap CEU Panel.
4076 Human Molecular Genetics, 2010, Vol. 19, No. 20
(23). Combining the AGP family-based transmission data with
control data also yielded no new loci (see Supplementary
Material). The peak association for MACROD2 remained at
rs4141463 (Table 2 and Supplementary Material), but the
P-value for association increased to 8.1 × 10
(strict diagnosis,
European ancestry). For the loci identified by primary analyses
of AGP data, the AGP, AGRE and control data taken together
(Table 2) had little effect on the significance level for
rs4141463 (P ¼ 3.7 × 10
for strict diagnosis, European ances-
try). In fact, the combined AGP, AGRE and control analyses
showed similar results to those from the combined AGP and
AGRE analysis (see Table 2), with the exception of rs4150167;
the P-value for this SNP rises to 2.1 × 10
. Looking over the
entire genome, analysis of the combined data did not reveal
compelling new loci (see Supplementary Material).
Genome-wide SNP association: exploratory analyses
To examine whether greater phenotypic homogeneity within
ASD could help identify common risk variants, we performed
a number of exploratory analyses examining specific sub-
groups of the ASD sample. In this study, we report in detail
two categorical variables: verbal status and IQ; see Methods
for description of exploratory categories. We also evaluated
parental origin effects through parental transmission. Sample
sizes are given in Table 1; results are given in Supplementary
Material, Table S4. None of our exploratory analyses detected
association below the threshold of 5 × 10
in the AGP dis-
covery sample alone. We do observe signals that are close
to the threshold (P , 1 × 10
, chosen strictly for heuristic
purposes) in the discovery sample in PLD5, POU6F2 and an
intergenic region on 8p21.3. Moreover, in a combined analysis
of the AGP and AGRE data, we observe three associations that
cross the P-value threshold: for verbal individuals, for SNPs
rs3784730 (in ST8SIA2) and rs2196826 (in PLD5); and, for
maternal parent of origin, rs9532931 (in a gene for an unchar-
acterized predicted protein KIAA0564). Importantly, these
findings would not be significant after correction for multiple
testing of diagnostic groups and sub-phenotypes. A summary
of all association signals at P , 1 × 10
in the exploratory
analyses are detailed in Supplementary Material, Tables S3
and S4); see Supplementary Material for results for SNPs
with association (P 5 × 10
Level of function, as measured by IQ, has been assumed to
be a major source of etiological heterogeneity for autism.
When we explored the impact of IQ on GWA results by split-
ting the sample by probands with IQ .80 and those with IQ
,70, no P-value exceeded the threshold for GWA significance
and none met criterion P , 1 × 10
Genome-wide SNP association: candidate loci
We compared our data with replicated candidate-gene studies,
which were derived from (19), as well as the recent GWA
reports that implicated intergenic intervals at the 5p14.1
CDH9CDH10 and 5p15.2 SEMA5A TAS2R1 loci, respect-
ively (20,21,24) (Supplementary Material, Tables S5 and S6).
Because the estimated effect sizes for these studies typically
fall in the range of 1.11.3, our power to replicate these findings
was low (Supplementary Material, Fig. S4) and some of the prior
candidate-gene studies made use of markers not well tagged by
SNPs in our study. We found some support for several prior
candidate loci, including CNTNAP2, RELN and SLC25A12
(P , 10
), but our analysis did not garner additional evidence
for either of the top findings from the prior GWA studies.
After testing 1 million SNPs for association with ASD, we
identified in one of our set of four primary analyses one
SNP, rs4141463, in MACROD2 crossing a preset threshold of
P , 5 × 10
. Three other SNPs crossed this threshold in the
context of exploratory analyses, making their interpretation
more difficult due to multiple testing. All of these results
spring from a relatively small sample size for GWA studies
(n 1369 families), limiting both our power to detect associ-
ation and the certainty of the associations detected. Unbiased
estimates of odds ratios detected by GWA studies are typically
in the range of 1.1 1.3; to have good power to detect such effect
sizes requires many thousands of samples, which is beyond the
reach of the autism genetics community at the moment. This
issue could at least partially explain why most genomic
regions with prior evidence of SNP associations for ASD risk
garner little support from our data (Supplementary Material,
Table S6). Moreover, the winner’s curse and shrinkage to the
mean (22,25,26) could explain the smaller odds ratios that we
estimated from the replication data (Table 2).
Keeping these caveats in mind, several results from our
study are potentially relevant to autism risk. The function of
MACROD2 (previously c20orf133) is largely unknown. The
protein contains a MACRO domain which is a high-affinity
ADP-ribose-binding domain that is important in multiple bio-
logical processes. Recent genome-wide studies have high-
lighted copy number variation at MACROD2 in an
individual with schizophrenia (27), brain infarct (28) and
brain volume in multiple sclerosis (29). Also 500 kb from
the association signal observed for ASD is FLRT3, which is
embedded in MACROD2. FLRT3 is a cell adhesion molecule
with functions in neuronal development.
It is interesting to consider that, while rs4141463 falls in a
MACROD2 intron, the precise location could be irrelevant to
its possible functional impact on ASD risk. Recent evidence
(30), yet to be corroborated, suggests that this SNP or one
of many correlated SNPs in this region (Fig. 1) acts to regulate
expression of PLD2. The observation becomes more note-
worthy in light of the fact that our exploratory analyses also
identify PLD5 as another locus possibly associated with
autism. PLD proteins could play an important role in risk
for autism. The protein derived from PLD2 has been shown
to regulate axonal outgrowth (31) and metabotropic glutamate
receptor signaling (32).
A second association signal of interest from the primary
analyses (Table 2 and Supplementary Material, Table S2)
involves a missense variation in the TAF1C gene (rs4150167;
G523R; P ¼ 1.0 × 10
). TAF1C (TATA box-binding
protein-associated factor 1C) is involved in the initiation of tran-
scription by RNA polymerase I. This process requires the for-
mation of a complex composed of the TATA-binding protein
(TBP) and three TBP-associated factors (TAFs) specific for
Human Molecular Genetics, 2010, Vol. 19, No. 20 4077
RNA polymerase I. TAF1C and its complex are displaced by
PTEN (33). Mutations in PTEN have been highlighted in a
number of cases of autism and related disorders (3439). A
caveat about the data for this SNP is worth noting: visual inspec-
tion revealed typical genotype clusters, yet the relatively
common allele (0.98) is over-transmitted, a pattern consistent
with poor genotyping quality.
From the exploratory analyses (Supplementary Material,
Table S3), we identify a number of loci as having noteworthy
association. One of the most appealing genes for risk for
autism is ST8SIA2, coding for a protein expressed very
highly throughout the mammalian brain (expression level or
density .90 for 14 out of 17 brain regions assessed in the
Allen Brain Atlas, http://www.brain-map.org/). Mice without
polysialyltransferases ST8SiaII and ST8SiaIV, which modify
neural cell adhesion molecule (NCAM1), show malformations
of major brain axon tracts (40). Loss of either ST8 protein
alone results in milder phenotypes. Inactivation of ST8SiaII
in mice alters axonal targeting, involving hippocampal infra-
pyramidal mossy fibers, and the mice show increased explora-
tion and diminished fear (40,41), behaviors of potential
relevance to autism. Learning and memory, mediated
through morphological synaptic plasticity, are also critically
dependent on NCAM polysialylation status but in a complex
way (42). Further studies are needed to determine the rel-
evance of these neurodevelopmental results to the genetics
of autism and identify the genetic variation affecting
expression or function of ST8SIA2. With regard to genetic
variation, in addition to the results found in our study, vari-
ation in ST8SIA2 has been associated with risk for schizo-
phrenia in Asian populations (43,44).
While we and others (20,21) find limited evidence that
common alleles affect risk for autism, the number of families
studied is still relatively small. Our findings appear to rule out
a common allele increasing relative risk by 2-fold or more.
Much larger samples will be required to detect subtle effects
on relative risk (e.g. 1.2), which is more typical of risk loci for
common diseases. With such low relative risk, replication of
true positive findings is further complicated by chance findings,
as well as differences in ascertainment. These challenges are not
unique to common variants. The same challenges are faced when
searching for rare sequence mutations and CNVs affecting risk
for ASD. Moreover, our ultimate goal is to integrate results
across the range of rare to common variation, thereby describing
the genetic architecture of autism. This will require larger
cohorts comprised of individuals exhibiting the relatively strin-
gent ASD phenotype of this study, as well as an unselected
group more representative of the general ASD population,
both being examined at the highest resolution for CNVs, rare
sequence variation and common alleles. The heterogeneity of
ASD will continue to complicate ameliorative opportunities;
however, the identification of risk variants could reveal target
gene pathways amenable for therapeutic intervention.
Sample collection and ascertainment
Diagnostic classes. For these analyses, we primarily grouped
families into two nested diagnostic classes (strict and spectrum
ASD) based on proband diagnostic measures (45). To qualify
for the strict class, affected individuals met the criteria for
autism on both primary diagnostic instruments, the ADI-R
(2) and the ADOS (3). In addition to individuals meeting cri-
teria for autism, a spectrum class included all individuals who
were classified as ASD on both the ADI-R and ADOS or who
were not evaluated on one of the instruments but were diag-
nosed with autism on the other instrument.
AGRE cohort. One additional family-based autism cohort was
evaluated in this study. The AGRE sample consists of families
in which a proband and often one or more siblings are diag-
nosed with ASD (46). A total of 595 families (1086 probands)
that were shown to be independent of the AGP sample were
identified for replication.
SAGE control cohort. A control group, namely subjects from
the Study on Addiction: Genetics and Environment (SAGE),
was chosen because it was genotyped with Illumina Human
1M-single Infinium BeadChip (23). This cohort consisted of
1965 control subjects (from the larger SAGE casecontrol
study). The consented sample included 31% males and 69%
females, with mean age of 39.2 (SD 9.1), and 73% subjects
self-identified as European-American (Caucasian), 26% as
African-American and 1% as other (http://zork.wustl.edu/gei/
study_description.htm). Both raw intensities and genotypes
were available through NHGRIdbGaP (http://www.ncbi.
92.v1.p1). The SAGE control subjects have had exposure to
alcohol (and possibly to other drugs), but did not meet the cri-
teria for any illicit drug dependence.
Genotyping. Samples were genotyped using the Illumina
Human 1M-single Infinium BeadChip. We performed strin-
gent, uniform QC procedures on the resulting data. The Illu-
mina Human 1M-single Infinium BeadChip contains a total
of 1 072 820 markers (50-mer probes) for SNP and CNV ana-
lyses. Samples were processed using the manufacturer’s rec-
ommended protocol with no modifications for Infinium II
arrays, and BeadChips were scanned on the Illumina BeadAr-
ray Reader using default settings. Analysis and intra-chip nor-
malization were performed using Illumina’s BeadStudio
software v.3.3.7, with a GenCall cutoff of 0.1. Built-in con-
trols, both sample independent (including staining controls,
extension controls, target removal controls and hybridization
controls) and sample dependent (including stringency controls,
nonspecific binding controls and nonpolymorphic controls),
were inspected to assess the quality of the experiment. For
genotype calling, we followed the manufacturer’s protocols
and used technical controls. Trios consisting of an affected
offspring and both parents were genotyped, and in total geno-
typing was completed for 4683 individuals from 1558
families. For the control sample, 1880 individuals were geno-
typed on the Illumina Human 1M-single Infinium BeadChip,
as described elsewhere (23).
The AGRE sample was genotyped on the Illumina
HapMap550 array (20), which yields genotypes for roughly
550 000 SNPs of the 1 million contained on the 1M chip.
To make these data comparable with the 1M platform, we
inferred the missing SNP genotypes by using three sources
4078 Human Molecular Genetics, 2010, Vol. 19, No. 20
of information: haplotypes called from trios genotyped on the
1M, haplotypes called from the HapMap550 genotypes and a
small set of 105 samples that were genotyped on both plat-
forms and yielded high-quality genotypes. This smaller set
of overlapping samples was used to evaluate the accuracy of
inferred genotypes. We used Beagle 3.0.1 to call haplotypes
and infer missing genotypes (47). Because Beagle 3.0.1
allows only families with a single offspring, we created trios
from multiplex families; inferred missing genotypes; then,
after putting family data back together, resolved inconsisten-
cies when possible and ‘zeroed’ inconsistencies otherwise.
Using the 105 samples genotyped on both platforms, we
assessed imputation accuracy; imputed genotypes for an
SNP were retained only when none of the called genotypes
were discrepant with the 1M genotypes. Following this QC
process, each sample from the AGRE data set contained gen-
otypes from the HapMap550 and 248 642 additional imputed
genotypes for subsequent analysis.
Association analysis
Genetic QC for association analysis. As a first QC step prior to
GWA analysis, probands from 80 families were removed
because they either carried chromosomal abnormalities, exhib-
ited chromosomal cell line artifacts or, based on literature
reports, had highly penetrant ASD CNVs. In broad outline,
subsequent QC for association analyses was performed at
family and individual levels, followed by QC for individual
We first assessed gender miscalls based on X chromosome
genotypes and allele calls for Y, adjusting gender when appro-
priate (e.g. miscoding) and dropping samples (e.g. Klinefelter
syndrome) or genotypes (e.g. loss of X in cell line) from the
X chromosome. We searched the database for duplicate
samples using a subset of 5254 SNPs that were independent
and had a .99.9% completion rate for genotypes at this QC
stage. Duplicates from four families were removed. Data
were subsequently checked for Mendelian errors, and 19
families with large numbers of errors were removed from
the analysis. In all other cases of Mendelian inheritance
errors, the SNPs were set to missing in the family exhibiting
the error. The fraction of complete genotypes per individual
was required to be 95% over the autosomes; 27 samples
fell below this criterion. Following this QC step, 4304 geno-
typed individuals were retained for 1445 pedigrees.
We then removed monomorphic SNPs or those with a gen-
otyping completion rate ,95%. After this step, 991 221 SNPs
were retained. We note that using a genotyping completion
rate of 95% or more allows some SNPs of poor genotyping
quality to enter the analysis; the alternative is to use a more
stringent completion rate, such as 99%, which has the advan-
tage of removing low-quality SNPs at the cost of removing
some high-quality SNPs. Recognizing the tradeoff, we chose
to use the less stringent criterion for association analysis,
and follow up SNPs with small P-values, by manual inspection
of genotype clusters. However, for the figures in the manu-
script, we use the more stringent criterion for genotyping com-
pletion rate, which more accurately reflects the final results
after manual inspection of genotype clusters.
Ancestry was then determined for the proband by using
5239 widely spaced, independent SNPs that had a genotype
completion rate of 99.9%. The software used was Spectral-
GEM (48), which estimated five significant dimensions of
ancestry (Supplementary Material, Fig. S1). Subsequent clus-
tering on the dimensions of ancestry resulted in six clusters:
three clusters of European ancestry, with n ¼ 824, 353 and
87; and three clusters reflecting other major ancestral
groups, with n ¼ 68, 54 and 35 (e.g. African and Asian); see
also Supplementary Material, Figure S1. The major European
cluster (n ¼ 824) was used to determine minor allele frequen-
cies (MAFs), to evaluate Hardy Weinberg equilibrium
(HWE) and Wright’s Fst among the genotyping sites. (Note,
however, that all three European clusters were used for the
ultimate association analyses of this ancestry.) Specific SNPs
were eliminated on the basis of the genotypes in this homo-
geneous European cluster for the following reasons: 5499
were monomorphic; 2102 for completion rate ,95%;
132 894 for MAF ,0.01; 7734 for HWE P , 0.005; and 89
for Fst . 0.02. Following this QC step, there were 842 348
SNPs available for association analysis. In this final
SNP-based edit, six more individuals had a genotype com-
pletion rate of ,95%. Merging diagnostic information with
genotype information to determine informative families
yielded 1369 families with complete genotype data for
parents and offspring; with at least one genotyped offspring
carrying an ASD diagnosis (16 families had more than one
genotyped, affected child).
Genetic association analyses. Family-based analyses were first
performed using FBAT, which allows for rapid calculation of
statistics under additive, dominant and recessive genetic
models. We do not present, here, the results for the dominant
or recessive models because they did not contribute meaning-
fully to the results. However, to implement more flexible ana-
lyses, such as parent-of-origin analyses, we also used an
in-house program written for family-based association (49)
that implements methods described by Cordell et al.(50).
Comparisons of the in-house program to FBAT results,
when appropriate, yielded excellent agreement (unpublished
data). A priori, we planned four principal analyses, all under
the additive model: spectrum versus strict diagnosis by all
ancestries versus European ancestry. These four analyses
covered the extremes of phenotype and ancestry. Numerous
exploratory analyses were also performed under an additive
genetic model: parent of origin analysis considering paternal-
and maternal-specific transmissions for both strict and spec-
trum diagnostic classes; and for the largest, spectrum sets,
we stratified by proband’s verbal/non-verbal status (51).
To determine whether the level of cognitive function, as
measured by IQ, was an important covariate for heterogeneity,
we split probands according to IQ into four groups: (i) those
with IQ . 80; (ii) those with 80 IQ 70; (iii) those with
70 . IQ . 25; and those with IQ 25. For GWA analyses,
we used only Groups (i) and (iii), which had the largest
sample size. IQ was measured in various ways by the different
recruitment centers, but for our purposes we used verbal, non-
verbal (performance) and full-scale IQ assessments. If an indi-
vidual’s score was .80 for any of these three measures, the
proband was classified into the above 80 group; otherwise,
Human Molecular Genetics, 2010, Vol. 19, No. 20
providing IQ was evaluated on at least two measures and none
were 70, the proband was classified into one of the below 70
groups. Sample sizes for principal analyses are given in
Table 1.
To enhance power for GWA tests, two additional data sets
were combined with the AGP data. We analyzed the AGRE
data using family-based analyses and the AGRE and AGP
data combined using mega-analyses. All primary analyses
were performed with both data sets. We limited exploratory
analyses to these nine: broad diagnostic group; verbal and non-
verbal status by the diagnostic groups and by ancestry. For the
primary analyses, we also analyzed two other sets of combined
samples: AGP trios together with AGRE families; AGP trios
together with unrelated SAGE controls; and all three data
sets. The method to analyze control and family-based data
(49) builds on two related ideas: matched casecontrol analy-
sis using conditional logistic regression (e.g. 52) and the
natural connection between family-based analysis and con-
ditional logistic regression of alleles found in probands (the
transmitted alleles) and matched pseudo-controls (formed
from transmitted and un-transmitted alleles) (49). Unrelated
SAGE controls were matched by genetic ancestry to probands
and combined with the ‘pseudo-controls’ produced by the
family-based analysis: first, by spectral analysis, we estimated
the genetic ancestry of probands and unrelated controls (48);
then, using the optimal matching algorithm, we formed geneti-
cally homogeneous strata (52), each consisting of a single
proband and one or more unrelated controls. In those strata
where a single control was matched with more than one
proband, the control was paired with the best match in the
stratum and the remaining probands each form their own
stratum. Finally, within each stratum, we contrasted the geno-
type of the proband with the genotypes of the matched controls
and pseudo-controls via conditional logistic regression.
Supplementary Material is available at HMG online. Raw data
from ASD family (Accession: phs000267.v1.p1) genotyping
are at NCBI dbGAP. A file containing results for SNPs with
association P 0.001 are provided at HMG online for all
primary and exploratory analyses.
The authors gratefully acknowledge the families participating
in the study.
Conflict of Interest statement. None declared.
This research was primarily supported by Autism Speaks
(USA), the Health Research Board (HRB, Ireland), The
Medical Research Council (MRC; UK); Genome Canada/
Ontario Genomics Institute and the Hilibrand Foundation
(USA). Additional support for individual groups was provided
by the US National Institutes of Health [HD055751,
HD055782, HD055784, HD35465, MH52708, MH55284,
MH057881, MH061009, MH06359, MH066673, MH077930,
MH080647, MH081754, MH66766, NS026630, NS042165,
NS049261]; the Canadian Institutes for Health Research
(CIHR), Assistance Publique - Hoˆpitaux de Paris (France),
Autistica, Canada Foundation for Innovation/Ontario Inno-
vation Trust, Deutsche Forschungsgemeinschaft (grant: Po
255/17-4) (Germany), EC Sixth FP AUTISM MOLGEN, Fun-
o Calouste Gulbenkian (Portugal), Fondation de France,
Fondation FondaMental (France), Fondation Orange
(France), Fondation pour la Recherche Me´dicale (France),
o para a Cieˆncia e Tecnologia (Portugal),
GlaxoSmithKline-CIHR Pathfinder Chair (Canada), the Hos-
pital for Sick Children Foundation and University of
Toronto (Canada), INSERM (France), Institut Pasteur
(France), the Italian Ministry of Health [convention 181 of
19.10.2001], the John P. Hussman Foundation (USA),
McLaughlin Centre (Canada), Netherlands Organization for
Scientific Research [Rubicon 825.06.031], Ontario Ministry
of Research and Innovation (Canada), Royal Netherlands
Academy of Arts and Sciences [TMF/DA/5801], the Seaver
Foundation (USA), the Swedish Science Council, The
Centre for Applied Genomics (Canada), the Utah Autism
Foundation (USA) and the Wellcome Trust core award
[075491/Z/04 UK]. We wish to acknowledge SAGE as part
of this study. Funding support for the Study of Addiction:
Genetics and Environment (SAGE) was provided through
the NIH Genes, Environment and Health Initiative [GEI]
(U01 HG004422). SAGE is one of the genome-wide associ-
ation studies funded as part of the Gene Environment Associ-
ation Studies (GENEVA) under GEI. Assistance with
phenotype harmonization and genotype cleaning, as well as
with general study coordination, was provided by the
GENEVA Coordinating Center (U01 HG004446). Assistance
with data cleaning was provided by the National Center for
Biotechnology Information. Support for collection of datasets
and samples was provided by the Collaborative Study on the
Genetics of Alcoholism (COGA; U10 AA008401), the Colla-
borative Genetic Study of Nicotine Dependence (COGEND;
P01 CA089392), and the Family Study of Cocaine Depen-
dence (FSCD; R01 DA013423). Funding support for genotyp-
ing, which was performed at the Johns Hopkins University
Center for Inherited Disease Research, was provided by the
NIH GEI (U01 HG004438), the National Institute on
Alcohol Abuse and Alcoholism, the National Institute on
Drug Abuse, and the NIH contract ‘High throughput genotyp-
ing for studying the genetic contributions to human disease
(HHSN268200782096C). Funding to pay the Open Access
Charge was provided by Autism Speaks.
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    • "There are some common variants identified in several large genome wide association studies – rs4307059 in the 5p14.1 region (Wang et al., 2009), rs10513025, rs10513026, rs16883317 in the 5p15.2 (Weiss et al., 2009), rs4141463 in the 20p12.1 region (Anney et al., 2010), and rs936938, rs6537835, rs1877455 in the 1p13.2 region (Xia et al., 2014 ). "
    [Show abstract] [Hide abstract] ABSTRACT: The extreme genetic heterogeneity of autism spectrum disorder (ASD) represents a major challenge. Recent advances in genetic screening and systems biology approaches have extended our knowledge of the genetic etiology of ASD. In this review, we discuss the paradigm shift from a single gene causation model to pathway perturbation model as a guide to better understand the pathophysiology of ASD. We discuss recent genetic findings obtained through next-generation sequencing (NGS) and examine various integrative analyses using systems biology and complex networks approaches that identify convergent patterns of genetic elements associated with ASD.
    Full-text · Article · Jun 2016
    • "(Ben-Shachar et al., 2009), but common variation has been implicated in upwards of 50% of cases of ASD (Gaugler et al., 2014). Although no SNP has achieved genomewide significance for ASDs, gene enrichment studies have given insight into a number of pathways associated with ASDs (Anney et al., 2010; Pinto et al., 2014 ). Some examples are: " neuronal signaling and development " , " synaptic function " and " chromatin regulation " (Pinto et al., 2014 ). "
    [Show abstract] [Hide abstract] ABSTRACT: Autism spectrum disorders (ASDs) are a group of debilitating neurodevelopmental disorders thought to have genetic etiology, due to their high heritability. The endosomal system has become increasingly implicated in ASD pathophysiology. In an attempt to summarize the association between endosomal system genes and ASDs we performed a systematic review of the literature. We searched PubMed for relevant articles. Simons Foundation Autism Research Initiative (SFARI) gene database was used to exclude articles regarding genes with less than minimal evidence for association with ASDs. Our search retained 55 articles reviewed in two categories: genes that regulate and genes that are regulated by the endosomal system. Our review shows that the endosomal system is a novel pathway implicated in ASDs as well as other neuropsychiatric disorders. It plays a central role in aspects of cellular physiology on which neurons and glial cells are particularly reliant, due to their unique metabolic and functional demands. The system shows potential for biomarkers and pharmacological intervention and thus more research into this pathway is warranted.
    Article · Apr 2016
    • "This indicates that while ST8SIA2 may contain common variation that increases risk for autistic traits, heterozygous deletion of this gene appears to be rare in patients with autism. However, given the previous association of ST8SIA2 with autism spectrum disorder, its involvement in the orchestration of early brain neurodevelopment and plasticity, and the very early onset nature of some behaviors in our patient, we hypothesize that haploinsufficiency of ST8SIA2 contributes to the phenotype of our patient [Rutishauser, 2008; Anney et al., 2010]. Neither C15orf32 nor FAM174B are OMIM-listed, and there is little published about their function. "
    [Show abstract] [Hide abstract] ABSTRACT: We present a patient with a behavioral disorder, epilepsy, and autism spectrum disorder who has a 520 kb chromosomal deletion at 15q26.1 encompassing three genes: ST8SIA2, C15orf32, and FAM174B. Alpha-2,8-Sialyltransferase 2 (ST8SIA2) is expressed in the developing brain and appears to play an important role in neuronal migration, axon guidance and synaptic plasticity. It has recently been implicated in a genome wide association study as a potential factor underlying autism, and has also been implicated in the pathogenesis of bipolar disorder and schizophrenia. This case provides supportive evidence that ST8SIA2 haploinsufficiency may play a role in neurobehavioral phenotypes.
    Full-text · Article · Apr 2015
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