Download genomic rearrangements in autism spectrum disorders: identification

GENOMIC REARRANGEMENTS IN AUTISM SPECTRUM
DISORDERS: IDENTIFICATION OF NOVEL CANDIDATE GENES
by
PATRICK MALENFANT
A thesis submitted to the Department of Physiology
In conformity with the requirements for
the degree of Doctor of Philosophy
Queen’s University
Kingston, Ontario, Canada
(November, 2009)
Copyright © Patrick Malenfant, 2009
ABSTRACT
There is evidence from family studies for the importance of genetic factors in the
development of autism spectrum disorders (ASDs) but the identification of major genes has not
been achieved to date. There are several reports of deletions and duplications in individuals with
ASDs, some of which are not unique to an individual. In most cases, the frequencies and
relevance of these abnormalities are unknown, as they have been identified serendipitously in one
or a few individuals. My overall hypothesis was that such rearrangements would facilitate the
identification of “culprit” genes associated with ASDs by identifying a small chromosomal region
for candidate gene testing. I molecularly characterized two overlapping 2p15-2p16.1 deletions
detected in unrelated individuals with confirmed autistic disorder (Subject 1) or autistic features
(Subject 2), a 1.4Mb deletion on chromosome Xp22 (Subject 1) and a duplication of chromosome
7q11.23, reciprocal to the Williams-Beuren Syndrome (WBS) deletion, in one individual with an
ASD (Subject 3). Using real-time semi-quantitative PCR, I screened a total of 798 individuals
with an ASD and 186 healthy controls for the presence of similar abnormalities. No additional
cases were identified in either group. Subsequently, I selected 6 genes [Orthodenticle homolog 1
(OTX1), Variable charge, X-linked (VCX), Neuroligin 4, X-linked (NLGN4X), Syntaxin 1A
(STX1A), Cytoplasmic linker 2 (CYLN2) and General transcription factor IIi (GTF2i)], based on
their function and localization within or in the vicinity of the rearrangements and tested them for
association with ASDs. Although there was no evidence for association for any marker or
haplotype in most of the genes tested, this was not so for GTF2i. Haplotype transmission
disequilibrium testing revealed an increased transmission, from healthy parents to their affected
offspring, of the common alleles of one marker and one haplotype in GTF2i (P = 0.0010 and
0.0005, respectively). This gene encodes a brain-expressed transcription factor previously
implicated in the mental retardation associated with WBS. Based on these findings, I propose
ii
that, although the genomic rearrangements reported herein are not a common cause of ASDs, the
GTF2i gene within the WBS critical region is important in the aetiology of autism.
iii
CO-AUTHORSHIP
The inclusion of co-authors reflects the fact that experimental data was obtained from active
multidisciplinary collaboration between researchers. The ideas, development and writing up of
this thesis was the responsibility of myself, the candidate, working within Queen’s University
under the supervision of Dr. Jeanette JA Holden.
Contribution of authors:
•
Clinical assessment of the patients was carried out by Dr ME Suzanne Lewis (University of
British Columbia).
•
Array-CGH experiments were performed by Dr Ying Qiao and Chansonette Harvard, under
the supervision of Drs ME Susanne Lewis and Evica Rajcan-Separovic (University of
British Columbia).
•
I, the candidate, performed all other laboratory procedures and data analyses as presented
in Chapters 2 and 3.
(This text was modified from the “General Declaration” of co-authorship of the Monash Graduate
School; http://www.monash.edu.au/phdschol/forms/pginfo/coauthor.rtf)
iv
To my brother Nicolas.
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ACKNOWLEDGEMENTS
This work would not have been possible without the support and encouragement of several
colleagues and friends. I wish to express my gratitude to my supervisor, Dr Jeanette Holden, for
her support and guidance. I would like to extend my appreciation to all ASD-CARC team
members. I am particularly grateful to Dr Xudong Liu and Dr Joe Hettinger for their friendship
and professional input. My thanks go out to Dr Suzanne Lewis and Dr Evica Rajcan-Separovic as
well as two members of their groups, Dr Ying Qiao and Chansonette Harvard for their invaluable
collaboration. I am also very grateful to Chris Hall for her technical support and for keeping me
informed of all the good gigs in town. I must also express my gratitude to the families who
participated in our research.
I wish to thank the members of the Department of Physiology. My special thanks go to
Karen Babcock and Mary Wales for their constant help. I am very grateful to the members of my
advisory committee, Dr Chris Ward, Dr Mark Sabbagh, Dr Neil Magoski and Dr Virginia Walker
for their advice and encouragement. I owe much appreciation to Dr Walker without whose kind
assistance, this thesis would not appear in its present form.
To my parents, Francyne and Guy on whose constant encouragement, love and support I
have relied throughout my undergraduate and graduate studies, thank you for being a constant
and active presence in my life. And last but not least, my dear Josée, the greatest occupational
therapist in the whole world, thank you for the incredible amount of patience you’ve had with me
over the years and for helping me live my dreams. Je t’aime.
This work was supported by an Ontario Mental Health Foundation (OMHF) scholarship to
me, a CIHR-IHRT grant (#43820) to JJAH, an OMHF grant to JJAH, a CIHR grant (RT-64217;
PI: MESL, MOP 74502; PI: ERS), Michael Smith Foundation for Health Research (MESL and
ERS), and on-going support from Ongwanada.
vi
TABLE OF CONTENTS
ABSTRACT....................................................................................................................... ii
CO-AUTHORSHIP ......................................................................................................... iv
ACKNOWLEDGEMENTS ............................................................................................ vi
TABLE OF CONTENTS ............................................................................................... vii
LIST OF TABLES ........................................................................................................... xi
LIST OF FIGURES AND ILLUSTRATIONS ........................................................... xiii
LIST OF ABBREVIATIONS ....................................................................................... xiv
CHAPTER 1 General Introduction ................................................................................ 1
1.1 Autism Spectrum Disorders.................................................................................................... 1
1.1.1 Diagnosis ............................................................................................................................ 2
1.1.2 Epidemiology and high-risk groups.................................................................................... 3
1.1.3 Medical conditions associated with ASD ........................................................................... 4
1.1.4 Treatment ............................................................................................................................ 5
1.2 ASD as a genetic disorders ...................................................................................................... 6
1.3 Strategies for the identification of susceptibility genes......................................................... 8
1.3.1 Genome-wide scans ............................................................................................................ 9
1.3.1.1 Linkage analysis........................................................................................................... 9
1.3.1.2 Results from genome scans........................................................................................ 11
1.3.2 Association studies............................................................................................................ 12
1.3.2.1 Case-control association ............................................................................................ 12
1.3.2.2 Family-based association ........................................................................................... 13
1.3.2.3 Results from candidate genes studies......................................................................... 15
vii
1.3.2.4 Genome-wide association studies .............................................................................. 18
1.3.3 Structural variations .......................................................................................................... 20
1.3.4 Types of structural variations............................................................................................ 21
1.3.4.1 Segmental duplications .............................................................................................. 21
1.3.4.2 Translocations ............................................................................................................ 22
1.3.4.3 Submicroscopic deletions and insertions ................................................................... 23
1.3.4.4 Tandem repeats .......................................................................................................... 23
1.3.4.5 Inversions................................................................................................................... 24
1.3.5 Effects of structural variations .......................................................................................... 25
1.3.6 Methods for the study of CNVs ........................................................................................ 27
1.3.7 Chromosome abnormalities in ASD ................................................................................. 27
1.4 Hypothesis and objectives ..................................................................................................... 33
1.5 Patients with rearrangements............................................................................................... 36
1.5.1 Subject 1 with del(2)(p15-p16.1) and del(x)(p22.2-p22.3)............................................... 36
1.5.2 Subject 2 with del(2)(p15-p16.1) ...................................................................................... 36
1.5.3 Subject 3 with dup(7)(q11.23) .......................................................................................... 37
1.6 Deletions at 2p15-p16.1.......................................................................................................... 38
1.6.1 Background....................................................................................................................... 38
1.6.2 OTX1 as a candidate gene for autism ............................................................................... 39
1.7 Deletion at Xp22.2-p22.3 ....................................................................................................... 42
1.7.1 Background....................................................................................................................... 42
1.7.2 NLGN4 and VCX as candidate genes for ASD................................................................ 42
1.7.2.1 NLGN4 ...................................................................................................................... 42
1.7.2.2 VCX ........................................................................................................................... 45
1.8 Duplications at 7q11.23 ......................................................................................................... 47
1.8.1 Background....................................................................................................................... 47
1.8.2 STX1A, CYLN2 and GTF2i as candidate genes for ASD susceptibility ......................... 49
1.8.2.1 STX1A ....................................................................................................................... 49
1.8.2.2 CYLN2....................................................................................................................... 50
1.8.2.3 GTF2i......................................................................................................................... 52
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CHAPTER 2 Materials and Methods ........................................................................... 53
2.1 Clinical description ................................................................................................................ 53
2.1.1 Subjects with ASD and control group............................................................................... 53
2.2 Molecular analysis ................................................................................................................. 55
2.2.1 Real-time semi-quantitative PCR...................................................................................... 55
2.3 Association testing.................................................................................................................. 70
2.3.1 SNP genotyping ................................................................................................................ 70
2.3.2 Statistical analysis............................................................................................................. 70
2.4 Correction for multiple comparisons ................................................................................... 71
2.5 In silico analysis of breakpoints............................................................................................ 71
2.6 Sequencing.............................................................................................................................. 72
2.6.1 Primer design .................................................................................................................... 72
2.6.2 DNA preparation and sequencing..................................................................................... 72
2.6.3 Data analysis ..................................................................................................................... 73
2.6.4 Confirmation of mutations................................................................................................ 73
2.7 Informed consent ................................................................................................................... 73
CHAPTER 3 Results....................................................................................................... 79
3.1 Del(X)(p22.1) in Subject 1 ..................................................................................................... 79
3.1.1 Array CGH data and sqPCR confirmation........................................................................ 79
3.1.2 Breakpoints ....................................................................................................................... 79
3.1.3 Screening for additional cases of del(X)(p22.1) ............................................................... 84
3.1.4 Association of autism with the NLGN4X and VCX genes............................................... 84
3.2 Del(2)(p15-16.1) in Subjects 1 and 2..................................................................................... 88
3.2.1 Array CGH data and sqPCR confirmation........................................................................ 88
3.2.2 Breakpoints ....................................................................................................................... 88
3.2.3 Screening for additional cases of del(2)(p15-16.1)........................................................... 94
3.2.4 Association of autism with the OTX1 gene in the deleted region ..................................... 94
3.2.5 Data bank analysis of breakpoint regions ......................................................................... 94
3.3 Dup(7)(q11.23) in Subject 3 .................................................................................................. 98
ix
3.3.1 Screening for cases with duplications or deletions at the WBS critical region................. 98
3.3.2 Breakpoints and screening for additional cases ................................................................ 98
3.3.3 Family-based Association Tests...................................................................................... 102
3.3.4 GTF2i re-sequencing ...................................................................................................... 105
CHAPTER 4 Discussion ............................................................................................... 110
4.1 Choice of experimental procedures and statistical analysis............................................. 110
4.1.1 Real-time sqPCR – sensitivity and specificity................................................................ 110
4.1.2 Family-based vs case-control association ....................................................................... 111
4.1.3 Correction for multiple comparisons .............................................................................. 112
4.2 The genetics of ASDs ........................................................................................................... 114
4.3 Copy-number variations and autism spectrum disorders................................................ 116
4.4 Genomic rearrangements characterization and candidate genes testing ....................... 117
4.4.1 Del(X)(p22.31)................................................................................................................ 117
4.4.1.1 VCX ......................................................................................................................... 121
4.4.1.2 NLGN4X.................................................................................................................. 122
4.4.2 Del(2)(p15-p16.1) ........................................................................................................... 123
4.4.3 Dup(7)(q11.23) ............................................................................................................... 129
4.5 Conclusions and Recommendations ................................................................................... 134
LITERATURE CITED ................................................................................................ 139
x
LIST OF TABLES
Table 1.1
Techniques available for genome-wide screening of CNVs...................................... 28
Table 2.1
Diagnostic details for individuals with ASD. ............................................................ 54
Table 2.2
Markers used for screening for cases of 2p16, 7q11 and Xp22 abnormalities in
probands and controls. ............................................................................................... 56
Table 2.3
Proximal Xp22 breakpoint refinement in Subject 1................................................... 57
Table 2.4
Proximal Xp22 breakpoint refinement in Subject 1................................................... 59
Table 2.5
Proximal 7q11 breakpoint refinement in Subject 3.................................................... 61
Table 2.6
Distal 7q11 breakpoint refinement in Subject 3......................................................... 63
Table 2.7
Proximal 2p16 breakpoint refinement in Subjects 1 and 2. ....................................... 65
Table 2.8
Distal 2p16 breakpoint refinement in Subject 2......................................................... 67
Table 2.9
Distal 2p16 breakpoint refinement in Subject 1......................................................... 68
Table 2.10 Primer sequences and amplification conditions for the sequencing of the 35 exons of
GTF2i......................................................................................................................... 74
Table 2.11 Primers and restriction enzymes used to confirm the status of mutations identified by
sequencing through RFLP experiments. .................................................................... 78
Table 3.1
Distal Xp22 breakpoint refinement in Subject 1........................................................ 81
Table 3.2
Proximal Xp22 breakpoint refinement in Subject 1................................................... 82
Table 3.3
Genes deleted on chromosome Xp22.1 in Subject 1.................................................. 83
Table 3.4
Family-based association testing of single markers within the NLGN4X and VCX
genes with autism....................................................................................................... 85
Table 3.5
Family-based haplotype association for the markers in the NLGN4X gene in families
with ASD. .................................................................................................................. 86
Table 3.6
Family-based haplotype association for the markers in the VCX gene in families with
ASD............................................................................................................................ 87
Table 3.7
Proximal 2p16 breakpoint refinement in Subjects 1 and 2. ....................................... 90
Table 3.8
Distal 2p16 breakpoint refinement in Subject 1......................................................... 92
Table 3.9
Distal 2p16 breakpoint refinement in Subject 2......................................................... 93
Table 3.10 Family-based association testing of single markers within the OTX1 gene with
autism......................................................................................................................... 95
Table 3.11 Haplotype family-based association testing of markers within the OTX1 gene with
autism......................................................................................................................... 96
xi
Table 3.12 Estimated frequencies of haplotypes in the OTX1 gene in affected individuals and
controls....................................................................................................................... 97
Table 3.13 Proximal 7q11 breakpoint refinement in Subject 3.................................................. 100
Table 3.14 Distal 7q11 breakpoint refinement in Subject 3....................................................... 101
Table 3.15 Single-marker family-based association testing for the markers in the STX1A, CYLN2
and GTF2i genes in families with ASD. .................................................................. 103
Table 3.16 Family-based haplotype association for the markers in the STX1A, CYLN2 and
GTF2i genes in families with ASD.......................................................................... 104
Table 3.17 Families selected for GTF2i sequencing.................................................................. 106
Table 3.18 Potential sequence variations within the GTF2i gene detected by sequencing........ 108
Table 4.1
Genes uncovered by the deletions in Subjects 1 and 2 and their position on
chromosome 2.......................................................................................................... 124
xii
LIST OF FIGURES AND ILLUSTRATIONS
Figure 1.1 Transmission disequilibrium test. .............................................................................. 14
Figure 1.2 Deletions on chromosome 2 in Subjects 1 and 2 compared with those reported by
Chabchoub et al. (2008) and de Leew et al. (2008)................................................... 40
Figure 1.3 Williams Syndrome critical region on chromosome 7q11......................................... 48
Figure 3.1 Refinement of the breakpoints of the deletions on chromosome Xp22 in Subject 1
using real-time sqPCR. .............................................................................................. 80
Figure 3.2 Refinement of the breakpoints of the deletions in Subjects 1 and 2 using real-time
sqPCR. ....................................................................................................................... 89
Figure 3.3 Genomic overlap of the dup(7)(q11.23) with the typical WBS interval deletions and
with the rearrangements associated with ASD reported by others and genomic
overlap........................................................................................................................ 99
Figure 3.4 Exon sequencing of GTF2i electrophoregrams showing apparent heterozygous
substitutions in subjects with ASD .......................................................................... 107
Figure 3.5 RFLP analysis for status confirmation of mutations detected by sequencing in GTF2i
exons. ....................................................................................................................... 109
xiii
LIST OF ABBREVIATIONS
5-HT
Serotonin (5-hydroxytryptamine)
5-HTR3A
5-HT receptor 3A
5-HTT
5-HT transporter
AChE
Acetylcholinesterase
ACR
Acrosin
ADHD
Attention-deficit hyperactivity disorder
ADI-R
Autism Diagnostic Interview-Revised
ADOS-G
Autism Diagnostic Observation Schedule-Generic
AGRE
Autism Genetic Resource Exchange
AMY1A
Amylase alpha 1A
ARSE
Arylsulfatase E
AS
Angelman syndrome
ASD-CARC
ASD – Canadian American Research consortium
ASD
Autism Spectrum Disorder
B3GNT1
UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 1
BAC
Bacterial Artificial Chromosome
BDNF
Brain-derived neurotrophic factor
BZRAP1
Benzodiazapine receptor (peripheral) associated protein 1
CACNA1G
Calcium channel, voltage-dependent, T type, alpha 1G subunit
CCT4
Chaperonin containing TCP1 subunit 4
CDCV
Common disease – common variant
CDD
Childhood disintegrative disorder
CDH10
Cadherin 10
CDH9
Cadherin 9
CFTR
Cystic fibrosis transmembrane conductance regulator
CGH
Comparative genomic hybridization
cM
Centimorgan
CNP
Copy-number polymorphism
CNTN4
Contactin 4
CNTNAP2
Contactin-associated protein-like 2
xiv
CNV
Copy number variation
COMMD1
Copper metabolism (Murr1) domain containing 1
CYLN2
Cytoplasmic linker 2
DECIPHER
Database of Chromosomal Imbalance and Phenotype in Humans using
Ensembl Resources
DGV
Database of Genomic Variants
DISCO
Diagnostic Interview for Social and Communication Disorders
DLB
dndritic lamellar body
DLG4
discs, large homolog 4 (also known as PSD-952)
DSM-IV
Diagnostic and Statistical Manual of Mental Disorders 4th Edition
DZ
Dizygotic
EHBP1
EH domain binding protein 1
EN2
Engrailed 2
EV
Euchromatic variant
FANCL
Fanconi anemia, complementation group L
FBXO40
F-box protein 40
FDR
False discovery rate
FISH
Fluorescence in situ hybridization
FMR1
FRX Mental Retardation 1
FRX
Fragile X
FWER
Family-wise error rate
FXTAS
FRX Tremor and Ataxia syndrome
GABA
γ-aminobutyric acid
GABRA5
GABA receptor subunit A5
GABRB3
GABA receptor subunit B3
GABRG3
GABA receptor subunit G3
GKAP
Guanylate kinase-associated protein
GPR143
G protein-coupled receptor 143
GSTM1
Glutathione S-transferase M1
GTF2i
General transcription factor II, i
GWAS
Genome-wide association study
IBD
identical-by descent
xv
IBI
Intensive behavioral intervention
ICD-10
International Classification of Mental and Behavioral Disorders 10th
revision
ID
Intellectual disability
IGHD
Immunoglobulin heavy chain D
IQ
Intelligence quotient
KAL
Kallman syndrome
LCR
Low-copy repeat
LD
Linkage disequilibrium
LINE
Long interspersed element
LOC388954
Similar to 40S ribosomal protein SA (p40)
LOD
Logarithm of the odds
MAOA
Monoamine oxidase A
MAPH
Multiplex Amplifiable Probe Hybridisation
MDGA2
MAM domain containing glycosylphosphatidylinositol anchor 2
MDR
Multifactor dimensionality reduction
MeCP2
The methyl-CpG binding protein 2
MLPA
Multiplex ligation-dependent probe amplification
MLS
Multipoint maximum LOD score
MPX
Multiplex
MRI
Magnetic resonance imaging
MTNR1B
Melatonin receptor 1B
MZ
Monozygotic
NAHR
Non-allelic homologous recombination
NF1
Neurofibromatosis type 1
NLGN
Neuroligin
NLGN2
Neuroligin 2
NLGN3
Neuroligin 3
NLGN4
Neuroligin 4
NSF
N-ethylmaleimide-sensitive factor
OTX1
Orthodenticle homolog 1
PARK2
Parkinson disease 2
xvi
PDD
Pervasive Developmental Disorders
PDDBI
PDD Behavior Inventory
PDD-NOS
PDD – not otherwise specified
PET
Positron emission tomography
PEX13
Peroxisome biogenesis factor 13
PKU
Phenyketonuria
PWS
Prader Willi syndrome
RABL2B
RAS oncogene family-like 2B
RAI1
Retinoic Acid-Induced 1
REL
v-rel reticuloendotheliosis viral oncogene homolog (avian)
RELN
Reelin
RFWD2
Ring finger and WD repeat domain 2
RFLP
Restriction-fragment length polymorphism
RT-PCR
Reverse transcriptase PCR
RU1
Repeat unit 1
SHANK3
SH3 and multiple ankyrin repeat domains 3
SHOX
Short stature homeobox
SINE
Short interspersed element
SLC7A5
Solute carrier family 7, member 5
SMS
Smith-Magenis syndrome
SNAP25
25kDa synaptosome-associated protein
SNARE
Soluble NSF Attachment Protein Receptors
SNP
Single nucleotide polymorphism
sqPCR
Semi-quantitative PCR
STS
Steroid sulfatase
STX1A
Syntaxin 1A
TDT
Transmission disequilibrium test
TMEM17
Transmembrane protein 17
TPH1
Tryptophan hydroxylase 1
TPH2
Tryptophan hydroxylase 2
UBE3A
Ubiquitin protein ligase E3A
xvii
USP34
Ubiquitin specific peptidase 34
VAMP2
Vesicle-associated membrane protein 2
VCX
Variable charge, X-linked
VCY
Variable charge, Y-linked
WBSCR
Williams-Beuren syndrome critical region
XLI
X-linked ichtyosis
XPO1
Exportin 1
xviii
CHAPTER 1
General Introduction
1.1 Autism Spectrum Disorders
Autism is the most severe of Autism Spectrum Disorders (ASDs) a group of
neurodevelopmental disorders characterized by impairment in three core domains: reciprocal
social interaction, verbal and non-verbal communication, and repetitive and/or stereotypic
behaviours. Although autism was first described by Kanner in 1943 (reprinted in Kanner, 1968),
there have been references, in both fictional and historical literature dating back several centuries
ago, to individuals who apparently met ASD diagnostic criteria (Wing & Potter, 2002).
ASDs are highly heterogeneous in the intensity and diversity of symptoms present. They
are classified under Pervasive Developmental Disorders (PDD), an umbrella term describing a
group of neurodevelopmental disorders defined in the Diagnostic and Statistical Manual of
Mental Disorders 4th Edition (DSM-IV) (American Psychiatric Association, 1994) and
International Classification of Mental and Behavioral Disorders 10th revision (ICD-10) (World
Health Organization, 1992). PDD include, in addition to autistic disorder, Asperger syndrome,
Rett Syndrome, pervasive developmental disorder – not otherwise specified (PDD-NOS) and
childhood disintegrative disorder (CDD; also known as Heller's syndrome).
Individuals with ASD commonly present with delayed language, as well as alterations in
motor coordination and sensory perception. Regression, or loss of previously acquired language
and social skills, before 24 months of age has been noted in between 25 to 50% of ASD cases
(Rogers, 2004) and in most cases, development prior to regression was normal (Richler et al.,
2006).
Individuals with Asperger syndrome do not present with significant language delays and
usually show cognitive skills in the normal to above-normal range. They do, however, have
severe social deficits and exhibit repetitive behaviours. Individuals with PDD-NOS, on the other
1
hand, also present with impairments in social interactions but the severity of the symptoms does
not reach the “autism-cutoff” in at least one of the three core domains or the age of onset is later
than 36 months.
Rett syndrome occurs almost exclusively in girls and is associated with specific physical
features such as a deceleration of the rate of head growth and smaller hands and feet (Tsai, 1992).
The methyl-CpG binding protein 2 (MeCP2) gene, which maps to chromosome Xq28 has been
identified as the culprit gene for Rett syndrome (Van den Veyver & Zoghbi, 2001; Van den
Veyver & Zoghbi, 2002).
1.1.1 Diagnosis
Long delays (>20 months) between reported parental suspicion and clinical diagnosis have
been an ongoing problem, especially in higher functioning children (Mandell et al., 2005). Few
paediatricians routinely perform autism screening (Dosreis et al., 2006) and the average age at
diagnosis varies greatly in different geographic regions in North America (Mandell et al., 2005).
As an alternative or complement to the ICD-10 and DSM-IV criterion, rating scales have been
devised for ASD evaluation and these are used for both clinical and research purposes. In all
cases, the diagnostic is behaviour-based, and involves the evaluation of levels of impairment in
the three core domains through standardized interviews and direct observation assessments (Lord
et al., 1994; Lord et al., 2000). The most commonly used diagnostic tools are the Autism
Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994), a questionnaire to be answered by the
parents or caregivers of affected individuals, and the Autism Diagnostic Observation ScheduleGeneric (ADOS-G) (Lord et al., 2000), a direct observation of the patient’s behaviour.
These diagnostic tools are constantly being revised and refined to include the broader range
of impairments excluded from earlier versions. Reliable early diagnostic, before the age of three,
has become a reality only recently (Lord, 1995). Even today, early assessment remains imperfect
2
as many cases of diagnosis on the lower end of the spectrum, such as PDD-NOS, are later revised
and changed to a more severe autism or autistic disorder diagnostic (Lord et al., 2006). Given the
known positive impact of an early diagnosis on functional outcome (Richler et al., 2006; Turner
et al., 2006), improvements in diagnostic tools and early intervention methods has received a
tremendous amount of attention over the last decade.
1.1.2 Epidemiology and high-risk groups
Epidemiological studies have pointed to an increasing rate of ASD over the past decades
(Chakrabarti & Fombonne, 2001; Fombonne, 2002; Fombonne, 2003; Yeargin-Allsopp et al.,
2003). From reported prevalence of around 5/10 000 births in the 1970s and early 1980s (Wing &
Gould, 1979), estimated rates of ASDs have risen to between 1/166 and 1/250 births in the 2000s
(Chakrabarti & Fombonne, 2001; Yeargin-Allsopp et al., 2003). Although at least part of this
increase can be attributed to changes in diagnostic methods (Shattuck, 2006; Coo et al., 2008) and
an increased awareness in the public, the possibility that genetic and/or environmental factors
may be causing a true increase in prevalence can not be excluded. Baron-Cohen (2006) reported
that individuals with Asperger’s syndrome and high functioning autism appear to present with an
over-developed systemizing approach to the world and are likely to choose rule-based careers and
hobbies in highly systemized fields such as computer sciences, one of the fastest growing fields
over the last two decades. The author suggested that selective mating between these individuals
could explain increasing rates of ASD.
The male to female ratio is approximately 4:1, suggesting a possible imprinting effect
and/or involvement of the sex chromosomes (Smalley, 1997; Volkmar et al., 2004). A positive
correlation between ASD prevalence and socioeconomic status has been consistently reported
(Hoshino et al., 1982; Croen et al., 2002; Bhasin & Schendel, 2007) although it has long been
suspected that this association may result from ascertainment bias (Wing, 1980), with recent
3
reports supporting this hypothesis. For example, a study from Denmark, where health care access
is universal, found no association between autism prevalence and various indicators of
socioeconomic status such as parental wealth or education (Larsson et al., 2005). Moreover, an
Atlanta (Georgia, USA) study on children identified only in schools where services should be
accessible to all, reported no association between socioeconomic status and the rate of autism
(Bhasin & Schendel, 2007).
The relationship between maternal age and autism prevalence remains unclear. While some
studies reported an increased risk with increased maternal age (Croen et al., 2002; Hultman et al.,
2002; Glasson et al., 2004), others found no association (Eaton et al., 2001; Juul-Dam et al.,
2001). Other groups found an association between paternal age and autism and suggested it to be
the true risk factor (Burd et al., 1999; Lauritsen et al., 2005).
1.1.3 Medical conditions associated with ASD
About 10% of ASD cases co-occur with other disorders (Rutter et al., 1990; Cohen et al.,
2005). Intellectual disability (ID) has been estimated to be present in between 40 to 55% of
individuals with autism (Chakrabarti & Fombonne, 2001; Yeargin-Allsopp et al., 2003) and
recent estimates suggest that nearly 30% of teenagers with intellectual disabilities have autism
(Bryson et al., 2008). The rate of mental retardation is lower in individuals diagnosed with ASDs
other than autism: 3 of 53 children with PDD-NOS presented with mental retardation in the study
by Chakrabarti and Fombonne (2001). Of note, ID is an exclusion criteria for Asperger syndrome
(American Psychiatric Association, 1994).
Autistic symptoms have been observed in 2.5% to 30% of patients with Fragile X (FRX)
syndrome, an X-linked disorder associated with ID, mental retardation and mood instability
(Blomquist et al., 1985; Piven et al., 1991; Bailey et al., 1993; Pembrey et al., 2001; Reddy,
2005). FRX is caused by an alteration of the transcription of the Fragile X Mental Retardation 1
4
(FMR1) gene on chromosome Xq27.3, due to an expansion of a CGG repeat in the 5'-untranslated
region. Individuals typically have 5 to 40 CGG repeats; those with 55 to 200 repeats are said to be
premutation carriers, which can cause the Fragile X Tremor and Ataxia syndrome (FXTAS)
(Hagerman & Hagerman, 2004). Expansions beyond 200 repeats are referred to as full mutations.
These lead to hypermethylation of the FRM1 gene, which inhibits it transcription and subsequent
translation (Snow et al., 1993). It is the most common cause of inherited mental retardation,
occurring in ~1/4000 males and ~1/8000 females. Females generally present with a milder
phenotype. FRX patients are usually initially assessed because of apparent developmental delays
and/or autistic features; up to 30% of FRX patients are also diagnosed with ASD (Pembrey et al.,
2001; Reddy, 2005). Impairments common to ASD and FRX include difficulties with verbal and
non-verbal communications, stereotypic behaviours, social anxiety, gaze aversion and impaired
social reciprocity (Hessl et al., 2006).
Seizures (Tuchman & Rapin, 2002), tuberous sclerosis (Lewis et al., 2004), immune
system deregulation (Warren et al., 1996), gastrointestinal difficulties (Kuddo & Nelson, 2003),
phenyketonuria (PKU) and sleep deregulations (Polimeni et al., 2005) are among the other most
common conditions co-occuring with ASD. While individuals with ASD cannot also be
diagnosed with attention-deficit hyperactivity disorder (ADHD), as the two are mutually
exclusive in the DSM-IV, symptoms associated with ADHD are frequently observed in persons
with ASD (Reiersen & Todd, 2008).
1.1.4 Treatment
While the positive impact of intensive behavioral intervention (IBI) has been documented
for over two decades (Lovaas, 1987; Rogers & Vismara, 2008; Eikeseth, 2009), no medications
are available for the core symptoms of ASD. In approximately 40% of children with ASD,
however, pharmacological agents are used to treat co-occurring conditions such as short attention
5
span, hyperactivity, anxiety, aggressiveness, sleep problems and self-injurious behaviours
(Findling, 2005; Witwer & Lecavalier, 2005). In some cases, this improves the effectiveness of
IBI programs by decreasing interference with treatment of co-morbid symptoms. Alternative
medicine approaches are also commonly used but there is little to no evidence of their
effectiveness. Treatment is usually multidisciplinary, including speech and language therapy as
well as physical and occupational therapy. It aims at increasing social interaction and
communication abilities through behavioural and motor analysis followed by matching
intervention.
1.2 ASD as a genetic disorders
A strong implication of genetic factors to the risk of various psychiatric disorders,
including schizophrenia, bipolar disorder and ASD, has already been established (O’Donovan et
al., 2003; Kato 2007). Although ASD do cluster in families, simple Mendelian models of
inheritance cannot explain their transmission. Nonetheless, autism is regarded as a highly
heritable psychiatric disorder and the issue has switched, over the years, from determining
whether or not is has a genetic basis, to identifying the specific genes involved.
The importance of genetics to ASDs was established by a series of twin studies, the first of
which was published by Folstein and Rutter (1977). The authors gathered information on all
reported school age autistic twin pairs with at least one of the two affected with autism, in Great
Britain. They obtained data on 10 dizygotic (DZ) and 11 monozygotic (MZ) twin pairs. While
none of the DZ twin pairs were concordant for autism, four of the MZ twin pairs were, yielding a
proband-wise concordance rate of 53% (pair-wise concordance of 40%). Subsequent twin studies
showed between 60% and 91% concordance in monozygotic twins, and 0% to 10% concordance
in dizygotic twins, depending on whether a narrow or broad phenotype was considered (Bailey et
al., 1995; Le Couteur et al., 1996).
6
The recurrence rate in siblings was estimated to be 4.5% by Jorde et al. (1991). Bolton et
al. (1994) studied 99 autistic patients and found a 2.9% concordance for autism, and between
12.4% and 20.4% concordance for a broader spectrum, a rate that is far greater then the
prevalence in the general population. The risk to relatives diminishes rapidly as one moves out of
first degree relationships (Szatmari & Jones, 1999).
Taken together, twin and family studies clearly support a significant genetic component to
ASD. Because concordance is less than 100% in MZ twins, even when considering a broader
spectrum of symptoms, and because the range of symptoms varies among concordant siblings,
additional factors are likely involved in the etiology of these disorders (Bailey et al., 1995; Le
Couteur et al., 1996).
Several studies have aimed at identifying a genetic model for autism susceptibility. The
first step for such identification is the investigation of the pedigrees of individuals with ASD.
Given the low reproduction rate of individuals with ASD, it would be expected that evolutionary
forces would eliminate genetic variations with a strong phenotypic effect. Even mildly deleterious
mutations are known to be unlikely to survive as common polymorphisms on the human genome
(Kryukov et al., 2007). Complex genetic disorders such as ASD are, therefore, suggested to result
from several, interacting, genetic alterations, each evolutionarily old, and each, with only a small
phenotypic effect (Ghosh & Collins, 1996; Risch & Merikangas, 1996; Cardon & Bell, 2001); see
common disease – common variant (CDCV) model in section 1.4.
Thus, although initial reports suggested that ASD follow a simple monogenic, autosomal
recessive, inheritance pattern (Ritvo et al., 1985), subsequent studies have pointed to more
complex models. For example, additive and/or epistatic inheritance modes were found to be
adequate in some studies (Jorde et al., 1991; Risch et al., 1999) and it is a well accepted
hypothesis that multiple interacting genes are involved in the etiology of ASD with estimates
7
ranging from three or four genes (Pickles et al., 1995) to as many as a hundred (Pritchard, 2001).
Also, given the phenotypic heterogeneity of ASDs, locus heterogeneity is also to be expected.
Locus heterogeneity refers to the possibility that different alterations in a gene may lead to similar
or identical phenotypic changes. For instance, cystic fibrosis has been associated with several
mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, some of
which are associated with more or less severe forms of the disorder (The Cystic Fibrosis
Genotype-Phenotype Consortium, 1993).
Obvious chromosome abnormalities account for ~5% of ASD cases (Weidmer-Mikhail et
al., 1998; Lauritsen et al., 1999; Konstantareas & Homatidis, 1999) and an additional ~5% arise
from brain-affecting disorders with an identified genetic cause such as tuberous sclerosis and
neurofibromatosis (Barton & Volkmar, 1998). The remaining ~90% of cases are idiopathic.
1.3 Strategies for the identification of susceptibility genes
A broad array of strategies is employed to identify risk genes involved in causing
idiopathic autism. Three of the most common are:
1)
Genome-wide scans
-
Uses polymorphic markers distributed across all chromosomes to identify
genomic regions harbouring potential susceptibility genes.
2)
Candidate gene approach
-
Focuses on genes selected based on their function, expression pattern,
potential relevance to ASD, as well as results obtained from genome scans.
-
The data is analysed to determine whether there is evidence for linkage or
association (see below for a brief description) with ASD in the sample
tested.
8
3)
Study of chromosome abnormalities in individuals with autism.
1.3.1 Genome-wide scans
Whole genome analysis uses linkage analysis to identify genomic regions that are more
likely to harbour ASD-causing genes. Polymorphic markers are used to measure cosegregation
between segments of chromosomes and disease status within families.
1.3.1.1 Linkage analysis
1.3.1.1.1 Parametric linkage analysis
Parametric linkage analysis is a method for determining whether there is evidence for cosegregation of alleles at marker loci with a phenotype. It takes advantage of the homologous
recombination that occurs during meiosis. It involves explaining the inheritance patterns of
genotypes observed in families where the phenotype is present (Kruglyak et al.; 1996).
Genetic markers located on different chromosomes are transmitted to the next generation
independently. Similarly, genetic markers located far apart on the same chromosome, will also be
transmitted independently because of the high probability of crossing-over between a pair of
homologous chromosomes during meiosis. In both cases, the transmission of alleles at each locus
from one generation to the next is independent and random. The loci are then said to be unlinked.
If, however, a marker is located near of a disease gene on the same chromosome, the
recombination rate between the two will be less than 50% and they will segregate together in the
majority of cases. These are said to be linked (Dawn Teare and Barrett; 2005).
Parametric linkage analysis is useful only when the disease model (eg: autosomal
dominant, autosomal recessive, etc) is correctly identified. Under a specific mode of inheritance,
the likelihood of specific alleles segregating with the disease is measured in a large family under
two hypotheses:
9
1)
Presence of linkage: the marker is linked to the disease locus at a particular
recombination rate (θ).
2)
Absence of linkage (null hypothesis): There is no linkage between the marker and the
disease locus (θ = 50%).
A likelihood ratio can therefore be calculated as the ratio of the likelihood of the data, if the
loci are linked at a given θ, to the likelihood of the data, if the loci are unlinked (θ = 50%). The
linkage of markers to phenotypic traits is usually quantified and expressed using the logarithm of
the odds (LOD) score, the log10 of this ratio. LOD scores provide a statistical estimation of the cosegregation of a disease with a given marker. To increase the power of linkage studies, several
markers can be tested at once in a multipoint linkage analysis (Morton, 1955).
1.3.1.1.2 Non-parametric linkage analysis (allele-sharing method)
The allele-sharing method is very useful when the inheritance mode is unknown or unclear
and therefore quite popular in the case of complex disorders. It studies whether affected relatives
inherit identical copies of a chromosomal region from a common ancestor (identical-by descent IBD) more often than expected by chance. Excess sharing of alleles IBD is measured by χ2 test.
Non-parametric linkage analysis is an alternative to the parametric method for identifying disease
locus over which it has a number of advantages (Dawn Teare and Barrett; 2005):
1)
No knowledge or assumption about the mode of inheritance is required.
2)
Small nuclear families are used rather than large pedigrees.
3)
It may be possible to estimate the fraction of a heterogeneous phenotype that is
determined by a given disease locus.
10
1.3.1.2 Results from genome scans
Several whole-genome scans for autistic disorders have been carried out (Auranen et al.,
2002; Barrett et al., 1999; IMGSAC, 1998; IMGSAC, 2001; Buxbaum et al., 2001; Cantor et al.,
2005; Liu et al., 2001; Philippe et al., 1999; Risch & Merikangas, 1996) and each pointed to
various regions of linkage, only a few of which overlap across studies. Suggestive linkage
(multipoint maximum LOD score; MLS > 1) was detected on 19 of the autosomal chromosomes
as well as the X chromosome and only two studies reached a genomewide significance with MLS
3.6 at marker D2S2188 on chromosome 2q31.1, and MLS 4.8 at marker D3S3037 on 3q26.32
(Auranen et al., 2002; IMGSAC, 1998; IMGSAC, 2001). The strongest linkage signals are also
relatively weak when compared to results obtained in Mendelian disorders. This has been
attributed to the complexity and heterogeneity of ASDs and of the samples tested. The regions of
interest identified in more than one genome scan are located on chromosomes 1p, 2q, 5q, 7q, 15q,
16p, 17q, 19p and Xq (reviewed in Klauck, 2006); the other loci being either unique or false
positives.
As indicated, one of the regions of greater interest is located on chromosome 7q which, in
addition to reaching significance in a meta-analysis of 6 independent genome scans (Trikalinos et
al., 2006), harbours chromosome abnormalities in some individuals with ASD. A preliminary
whole-genome scan in 36 affected sibling pairs and a subsequent study with 51 additional
families produced a MLS of 2.53 at 135 cM (IMGSAC, 1998). A follow-up study with a total of
125 sibling-pairs resulted in a multipoint MLS of 3.37 at 112 cM (IMGSAC, 2001). In a study of
75 families, the Collaborative Linkage Study of Autism reported a multipoint MLS of 2.2 at 104
cM (Barrett et al., 1999) and a MLS of 1.77 at 128 cM in a subsequent focal study of
chromosome 7q carried out with denser polymorphism coverage in 76 multiplex families
(Ashley-Koch et al., 1999). The region of interest on chromosome 7q remains broad;
11
approximately 60 centimorgans (cM), and contains several genes, as positive findings vary in
terms of localization and signal strength.
1.3.2 Association studies
Association studies are more powerful in detecting genes with small effects in complex
diseases than linkage studies (Risch & Merikangas, 1996). Case-control (or population-based)
association study is based on measuring allelic association between a marker and disease-causing
genetic variations. Allelic association refers to a significant increase or decrease in frequency of a
given allele in a disease or disorder. This association can be due to the fact that genetic markers
physically close to one another are likely to be inherited together from generation to generation in
a given population and are said to be in linkage disequilibrium (LD).
Haplotypes, a group of associated single nucleotide polymorphism (SNP) alleles in a
genomic region, are commonly used to increase the power of association studies. The HapMap
Project (www.hapmap.org) provides invaluable information on the frequencies and patterns of
association of roughly 3 million common SNPs on the human genome in four ethnic populations
(International HapMap Consortium, 2003). This allows the cost-efficient assessment of much of
the common haplotypic variation within a population at a given locus.
1.3.2.1 Case-control association
Case-control (or population-based) association compares the allelic distribution of a
genetic marker, or group of markers, between a group of affected individuals (cases) and a
control group. The χ2 test is used for determining the statistical significance of allele distribution
in the two groups.
12
Subject recruitment for population-based association studies is easier than for linkage and
family-based association studies as unrelated individuals are used rather than whole families. It is
therefore a widely used strategy for the identification of disease genes.
Careful matching of cases and controls and the study of homogeneous populations
represents the greatest challenge for population-based studies, as population stratification is
strongly detrimental to high-quality association results. The problems created by population
stratification or admixture has long been recognized, even in populations where no difference in
the prevalence of a disorder is observed between subgroups (Morton & Collins, 1998; Thomas &
Witte, 2002). This represents an even greater challenge in the case of complex, heterogeneous
disorders such as ASD.
1.3.2.2 Family-based association
The problem of population stratification can be overcome by using a family-based
association method, which uses non-transmitted alleles from the parents of the affected offspring
from nuclear families as internal controls and the transmitted alleles as the cases (Horvath et al.,
2001). The transmission disequilibrium test (TDT) is the most commonly used version of familybased association testing. It tests for distortion in the transmission of alleles from heterozygous
parents to affected child. The χ2 test is then used to test the null hypothesis that each allele is
transmitted with a probability of 50%, as expected by random Mendelian segregation. TDT can
also be tested on multiplex families as every child has an independent 50% probability of
inheriting either of the parent’s alleles. TDT is less powerful in the case of disorders caused by
rare variants as only a small percentage of the population carries the rare allele and few
heterozygotes are available for testing.
13
1-2
2-2
1-2
1-2
1-1
2-2
2-2
1-2
1-1
2-2
1-2
1-2
Figure 1.1 Transmission disequilibrium test. Four nuclear families, each with an affected child,
are illustrated. Homozygous parents (1-1 and 2-2 in this example) are not informative as
heterozygous parents (1-2) are needed to compare the frequencies of transmitted vs nontransmitted alleles. These parents can transmit either allele 1 or 2 to the affected child. Here, in
three families out of four, allele 1 (underlined) was transmitted from a heterozygous parent to the
affected child. The χ2 test is used to determine if the results obtained are statistically significant.
14
1.3.2.3 Results from candidate genes studies
Over the last decades, several dozens of candidate genes have been tested for association
with ASD. This can be attributed to the fact that over one third of all human genes are expressed
in the adult or developing brain (Boguski & Jones, 2004). Several neurotransmitters and
neuropeptides involved in neurodevelopment and neural function have thus been suggested as
candidate genes for ASD. For instance, there are multiple lines of evidence linking abnormalities
of serotonergic function to autism. A first report of elevated serotonin (5-HT) levels in the blood
of children with autism and ID (Shain & Freedman, 1961), was replicated by others and later
found to extend to first degree relatives of affected children (reviewed in Chugani, 2004).
Moreover, levels of platelet 5-HT were found to be higher in affected individuals from multiplex
families compared to simplex families, suggesting a genetic cause for serotonergic abnormalities
in autism.
Among the candidate genes tested to date are the tryptophan 2,3- dioxygenase, monoamine
oxidase A (MAOA) and the 5-HT transporter (5-HTT) genes. Results of studies on the 5-HTT
gene have been mixed. Although preferential transmission to affected offspring was reported by
Cook et al. (1997) for the short promoter variant associated with lower levels of transporter
molecules, and replicated by McCauley et al. (2004), other groups found increased transmission
of the long allele of the same variant (Klauck et al., 1997; Yirmiya et al., 2001). These reports are
in contrast to a series of studies that found little or no association between the 5-HTT gene and
autism (Maestrini et al., 1999; Zhong et al., 1999; Persico et al., 2000; Betancur et al., 2002; Kim
et al., 2002). Taken together, these studies do not strongly support a major role for 5-HTT in
determining risk for autism per se.
More recently, there have been reports of an association between variants in the 5-HTT
gene and severity of autistic behaviors in the social and communication domains, as well as rigid15
compulsive behaviour (Tordjman et al., 2001; Mulder et al., 2005). Other studies have examined
alterations in the metabolism of 5-HT that may be involved in serotonergic abnormalities. For
example, studies on genes coding for MAOA, an enzyme involved in the catabolism of 5-HT,
reported correlations between a low-activity MAOA allele and both lower intelligence quotient
(IQ) and severe autistic behaviour (Cohen et al., 2003a). Further, using positron emission
tomography (PET), Chugani et al. (1999) determined that 5-HT synthesis capacity in the brains of
children with autism was lower compared to control children, until the age of 5, suggesting that
genes involved in the synthesis of 5-HT may be important in the etiology of ASDs. Tryptophan
hydroxylase 1 (TPH1) encodes the rate-limiting enzyme in the synthesis of 5-HT from
tryptophan. Herault et al. (1993) did not find any evidence for an association between TPH1 and
autism. However the TPH1 gene is predominantly expressed in the pineal gland and
enterochromaffin cells of the gut. The identification by Walther et al. (2003) of a second TPH
gene, TPH2, located at 12q21.1 and expressed mainly in the mammalian brain, offers an
additional candidate gene for studying serotonergic pathways in the etiology of autism. While
Coon et al. (2005) found an association of two variants within the TPH2 gene with autism in 88
families, subsequent studies by other groups did not find any evidence for association (Ramoz et
al., 2006; Sacco et al., 2007).
Some of the other genes that have received the most attention are the reelin (RELN) and
engrailed (EN2) genes on chromosome 7q, a cluster of γ-aminobutyric acid (GABA) receptor
subunit genes (GABRB3, GABRG3 and GABRA5) within the Prader Willi / Angelman syndrome
(PWS/AS) region on chromosome 15q, and the neuroligin 3 and 4 (NLGN3 and NLGN4) genes,
located on the short and long arm of chromosome X, respectively. The role of NLGNs and other
genes of the neurexin-neuroligin pathway is discussed in more details in section 1.7.2.1 below.
16
The GABRB3, GABRG3 and GABRA5 genes have been extensively studied, with mixed
results. Two studies initially observed an association with autism of a microsatellite located in
intron 3 of GABRB3 (Cook, Jr. et al., 1997; Buxbaum et al., 2002). However, this finding was not
replicated in four subsequent studies (Maestrini et al., 1999; Salmon et al., 1999; Martin et al.,
2000; Curran et al., 2005). While some additional studies observed a modest association of
markers located in or around these three GABA receptor genes with autism (Martin et al., 2000;
Menold et al., 2001; McCauley et al., 2004; Ashley-Koch et al., 2006), others found no
significant association (Nurmi et al., 2001; Ma et al., 2005; Kim et al., 2006).
Polymorphisms in the oxytocin receptor gene (OXTR) have been associated with autism in
195 Chinese trios (Wu et al., 2005). Oxytocin is a neuropeptide that has been linked to processing
of social cues as well as social recognition and bonding in mammals (Insel & Young, 2000).
The RELN gene, which maps to chromosome 7q22, encodes a glycoprotein involved in
neuronal migration. It directs cortical layer formation during brain development and is also
expressed in the adult brain (Alcantara et al., 1998; D'Arcangelo et al., 1999). Persico et al.
(2001) found an association between autism and the large allele (>10 copies) of a polymorphic
CGG triplet repeat 5’ to the RELN gene in 177 families. This was supported by Zhang et al.
(2002) who tested 126 MPX families but not by others (Krebs et al., 2002; Bonora et al., 2003).
Association studies testing the interdependent contribution of candidate genes part of
biological pathways are beginning to emerge in the literature. Anderson et al. (2009) tested 10 5HT pathway genes for linkage and association in 403 ASD families. They genotyped 45 SNPs
and found association of one within the 5-HT receptor 3A (5-HTR3A) gene on chromosome 11.
Multifactor dimensionality reduction (MDR) (Ritchie et al., 2001) was used to detect multilocus
effects. A significant interaction between SNPs near the melatonin receptor 1B (MTNR1B) and
solute carrier family 7, member 5 (SLC7A5) genes, on chromosomes 11 and 16, respectively, was
17
detected. Abnormalities in 5-HT metabolism are commonly reported in autism but linkage and
candidate gene studies results have not been consistent.
For each gene presented above, evidence supports an association with autism. Yet, in each
case, this is weakened by contradictory results from replication studies. In order to obtain stronger
association results, geneticists have aimed at reducing sample heterogeneity by stratifying cases
into sub-groups based on phenotypic characteristics, such as language delay, behavioural
regression, presence of savant skills and insistence on sameness. This approach has allowed
increasings the linkage signal at chromosome 2q when individuals are subgrouped, based on the
presence of language delay (Spence et al., 2006). A strengthening of the linkage signal at 7q and
13q was obtained in analysis of families with a history of language delay (Bradford et al., 2001)
but this was not replicated by other groups (Spence et al., 2006). Similarly, Molloy et al.(2005)
found evidence for linkage at 21q and 7q in a sub-group of individuals with autism and
developmental regression but these finding was not replicated in a follow-up study (Parr et al.,
2006).
1.3.2.4 Genome-wide association studies
Genome-wide association studies (GWAS) may provide a more powerful approach to
identifying ASD susceptibility genes. Similarly to case-control candidate gene association
studies, GWAS compares allelic distribution in affected individuals and healthy controls. In this
case, however, the markers tested are distributed throughout the genome rather than limited to a
candidate gene or genomic region. Given that association studies offer greater power over linkage
for detecting susceptibility alleles of weaker effect (Risch et al., 1999), GWAS likely represents a
more adequate approach for genetic studies of ASD. Until recently, this approach was costprohibitive and technically challenging but GWAS reports have now begun to emerge in the
literature.
18
Using genome-wide association in 72 multiplex families, Arking et al. (2008) identified a
common variant in the Contactin-associated protein-like 2 (CNTNAP2) gene associated with ASD
in male-male families (families where all affected individuals were male). Results from other
studies also found evidence for association of CNTNAP2 with ASD (see section 1.7.2.1).
Ma et al. (2009) performed a GWAS in 438-family discovery dataset and a 487-family
replication group. A region on chromosome 5p14.1 showed significance in both groups and in the
joint analysis (both groups together). However, none of the markers tested in CNTNAP2 met
significance after correction. Association with 5p14.1 was also found by Wang et al. (2009) who
performed GWAS in more than 10 000 subjects of European descent. More specifically, alleles
from 6 SNPs located between the cadherin 10 (CDH10) and cadherin 9 (CDH9) genes were
associated with ASD. All 6 SNPs were located within a ~100 kb haplotype block, suggesting that
they are linked to the same variant. CDH9 and CDH10 encode transmembrane proteins from the
cadherin superfamily, which mediates calcium-dependent cell-cell adhesion.
Based on previously reported evidence of linkage of chromosome 17q11-q21 to ASD in
multiplex families (Cantor et al., 2005; Yonan et al., 2003), Stone et al. (2007) genotyped a dense
panel of SNPs (average intermarker distance of 6.1 kb) distributed across approximately half of
the linkage interval (13.7 Mb) in 219 unrelated trios. While a combination of nominally
significant haplotypes or single SNPs was observed, none could account for the initial linkage
signal. The authors suggested that this may be due to the presence of multiple, rare or common,
alleles, each contributing to the genetic risk for ASD.
Strom et al. (2009) covered the remainder of the linkage peak (12.6 Mb) using 1975 SNPs
at an average intermarker distance of 6.3 kb and found two markers (rs757415 and rs12603112)
within an interval containing the Calcium channel, voltage-dependent, T type, alpha 1G subunit
(CACNA1G) gene to be associated with ASD at a region-wide significant level. The two SNPs,
located in intron 9 of CACNA1G were in strong LD (r2 = 0.99), which explains why both revealed
19
similar association results. While relatively strong, the association was still insufficient to
explain, by itself, the strength of the linkage signal. Given that mutations in voltage-gated
calcium channels had previously been linked to ASDs (Splawski et al., 2004; Hemara-Wahanui et
al., 2005; Krey & Dolmetsch, 2007), CACMA1G was considered as the strongest candidate gene
for these disorders on the 17q11-q21 interval.
It should be noted that only a few GWAS have been published to date, which makes
comparison across studies difficult at the present
1.3.3 Structural variations
A potentially fruitful approach for the identification of autism-causing genes is the analysis
of chromosome abnormalities. There are several examples of genetic conditions where a
cytogenetic abnormality has led to the identification of genes associated with a disorder. Such
chromosomal abnormalities can lead to functional changes in various ways (Lupski and
Stankiewicz, 2005):
1) Dosage effect resulting from changes in copy number.
2) Disruption of a gene located at the breakpoints.
3) Positional effects.
4) Unmasking of a point mutation in the corresponding, nondeleted, homologous
chromosome.
The earliest form of polymorphism screenings were carried out with conventional
cytogenetics methods, using light microscopy and chromosome banding techniques (Seabright,
1971) and they led to the identification of several chromosomal variations in humans (Jacobs,
1977). Since then, there has been tremendous advancement in the detection of genetic variations
and their understanding thanks to the advent of high-throughput sequencing and screening
20
technologies. Such rearrangements range from large cytogenetic abnormalities (>1 Mb) to single
base pair changes. Although SNPs far outnumber the number of occurrences of larger
abnormalities, the latter still play a significant role in human genetic variability. Recent studies
estimated that the average deletion/duplication event is approximately 15 kb in size and occurs at
a rate of 0.14 event/generation (Tuzun et al., 2005; van Ommen, 2005). Therefore, structural
variations are estimated to account for approximately 2000 bp of novel genetic variation in each
individual, nearly 20 times more than the ~120 new single-base changes occurring between each
generation (Kondrashov, 2003). Also, SNPs are estimated to occur once every 1200 bp on the
human genome for a total of between 2 and 2.5 x 106 polymorphic base pairs (International
HapMap Consortium, 2005). Copy number variations (CNV) on the other hand, are estimated to
occur ~250 times in the genome. With an average size of ~15 kb, this represents a total of nearly
4 x 106 base pairs of copy number polymorphisms (Tuzun et al., 2005).
1.3.4 Types of structural variations
1.3.4.1 Segmental duplications
In situ and in silico analyses have shown that duplicated sequences compose approximately
5% of the human genome (Cheung et al., 2001; Bailey et al., 2002; Cheung et al., 2003).
Segmental duplications, or low-copy repeats (LCR), are segments of DNA, 1-500 kb in size, with
90-99% sequence identity, and are present in multiple copies in different sites within the genome
(Eichler, 2001). They are often interspersed throughout the entire genome but are particularly
enriched at pericentromeric and subtelomeric regions of chromosomes. Subtelomeric LCRs
account for ~40% and pericentromeric LCRs account for ~30% of the total (She et al., 2004;
Linardopoulou et al., 2005).
Several groups have reported a significant association between the presence of LCRs on
given genomic regions and the occurrence of chromosomal rearrangements, some of which are
21
associated with recurrent genomic disorders (Ji et al., 2000; Inoue & Lupski, 2002; Armengol et
al., 2003; Bailey et al., 2004; Sebat et al., 2004; Sharp et al., 2005). Because of their high
sequence homology and interspersed nature, LCRs serve as substrates for structural
rearrangements through non-allelic homologous recombination (NAHR) and therefore act as a
catalyst for chromosomal instability (Samonte & Eichler, 2002; Sharp et al., 2005; Tuzun et al.,
2005). NAHR between directly oriented repeats results in the deletion, duplication, or
translocation of the intervening genomic segments and recombination between inverted repeats
results in inversions. Color blindness, one of the first genetic traits to be mapped, was shown to
result, in a large number of cases, from unequal recombination between LCRs (Deeb, 2005).
In some cases, both chromosome rearrangements and point mutations in one of the genes
involved in the rearrangement can lead to the same neurologic disorder. For example, SmithMagenis syndrome (SMS), a disorder that has been associated with autism (Vostanis et al., 1994),
is caused by a 3.7 Mb interstitial deletion on chromosome 17p11.2 in 90% of cases, but the
disorder can also arise from mutations in the Retinoic Acid-Induced 1 gene (RAI1), located within
the commonly deleted region (Girirajan et al., 2005).
In addition to their propensity for rearrangements, LCRs can have other effects.
Asynchronous replication has been observed on genomic regions harbouring tandem duplications,
suggesting an alteration in the epigenetic state of such loci (Gimelbrant & Chess, 2006).
1.3.4.2 Translocations
Robertsonian translocations are whole arm exchanges between acrocentric chromosomes
(13, 14, 15, 21 and 22 in humans). They are found in 1 in 1000 individuals, making them one of
the most common chromosomal rearrangements (Hamerton et al., 1975). Although nearly 85% of
all Robertsonian translocations are rob(13q14q) or rob(14q21q), all possible combinations in
humans have been observed (Therman et al., 1989).
22
Reciprocal translocations that involve the exchange of genetic material between
chromosomes occur in about 1 in 625 births and have been associated with every chromosome.
Although most cases are unique to the families in which they segregate, some are relatively
common such as t(11;22)(q23;q11.2).
1.3.4.3 Submicroscopic deletions and insertions
Several different techniques, using array-based approaches, allow the screening of the
genome for submicroscopic genomic abnormalities. One of the first large-scale studies lead to the
identification of 76 loci associated with copy-number polymorphisms (CNP) (Sebat et al., 2004).
This was followed, the same year, by the identification, using Bacterial Artificial Chromosomes
(BAC), of 255 genomic regions that showed copy number variations (Iafrate et al., 2004). The
methods used in these studies provided a resolution of ~100 kb - 1 Mb. More recent strategies
such as high-density SNP arrays, which are based on the computational analysis of genotyping
errors and non-Mendelian allelic transmissions, now allow the detection of abnormalities of less
than 5 kb in size (Conrad et al., 2006).
Recent data from the Human Genome Mutation database suggests that approximately 5%
of Mendelian genetic diseases are associated with microdeletions or microduplications, which
represent the most frequent structural variations on the human genome (Armour et al., 2002;
Tuzun et al., 2005).
1.3.4.4 Tandem repeats
Tandem repeats are composed of variable numbers of serial repeated segments, which can
be up to several hundred thousand base pairs in size. In most cases, these repetitive elements are
not transcriptionnally active but a few genes such as glutathione S-transferase M1 (GSTM1) and
amylase alpha 1A (AMY1A) are known to be part of tandem repeats (Siwach & Ganesh, 2008).
23
Because of their repetitive nature, the expansion and contraction of tandem repeats is likely
mediated by NAHR.
1.3.4.5 Inversions
Inversions are a change in the orientation and/or localization of DNA segments within a
particular chromosome and are not generally associated with the gain or loss of genetic material.
They are considered balanced rearrangements and are not detected with array-based technologies,
which can only detect changes in DNA copy number. Although inversions can potentially disrupt
coding regions at the breakpoints, they are generally not associated with copy number changes
and may not be associated with significant phenotypic changes (Kleinjan & van, V, 1998).
Although their number has likely been underestimated so far because of this technical limitation,
inversions have nonetheless been associated with a few disorders or diseases (Bondeson et al.,
1995; Small et al., 1997; Gimelli et al., 2003).
There is evidence, for example, that inversions can be associated with an increased
susceptibility to further genomic rearrangements. Inversions at 15q11-q13 were found in 6/9
(67%) mothers of patients with Angelman syndrome associated with a maternal deletion of
15q11-q13. This compares to only 9% in the general population (Gimelli et al., 2003). Similarly,
one in three parents who transmitted a deletion of the Williams-Beuren syndrome critical region
(WBSCR) to their offspring have an inversion at that locus (Osborne et al., 2001). Although, in
theory, these inversions should also lead to an increased susceptibility to the reciprocal
duplication, there is no experimental data to support this statement yet.
Computational methods using data from paired-end sequence and the human reference
assembly now allows the identification of an increasing number of balanced rearrangements. A
study allowed the identification of 56 polymorphic inversions, ranging from 5 kb to 1.9 Mb, on
the human genome (Tuzun et al., 2005). In the majority of cases, the breakpoints of those
24
rearrangements mapped to segmental duplications and therefore likely occurred through NAHR.
More importantly, several inversions were found to be associated with the loss or gain of genetic
material at or near of the breakpoints (Newman et al., 2005; Tuzun et al., 2005).
Interestingly, large inverted repeated DNA segments have been found to be enriched on the
human X, but not the Y, chromosome, which could, theoretically, increase the occurrence of
further inversion events occurring through NAHR on chromosome X (Warburton et al., 2004).
1.3.4.5.1 Euchromatic variants
Unbalanced chromosome abnormalities that segregate without apparent phenotypic effects
have been described in several families (Barber, 2005). While most cases are observed in single
families, some occur more or less frequently in the general population and are termed
euchromatic variants (EV). These appear to be increased expansions of highly polymorphic copynumber variants and are highly enriched in pericentromeric regions of chromosomes, which are
the preferential integration sites of segmental duplications (Bailey et al., 2002), as well as, to a
lesser extent, in subtelomeres (Trask et al., 1998).
One such example is the amplification of a duplication cassette containing pseudogenes
GABRA5, Neurofibromatosis type 1 (NF1) and immunoglobulin heavy chain D (IGHD), located
in the pericentromeric region of chromosome 15q, proximal to the deletions associated with
PWS/AS. The segmental duplication is present in between one to four copies in the general
population but can be found in up to 20 repeated copies in individuals with cytogeneticallyvisible extra euchromatic material (Ritchie et al., 1998).
1.3.5 Effects of structural variations
Studies using BAC comparative genomic hybridization (CGH) arrays reported that 8001800 genes are subject to copy number variations (Iafrate et al., 2004; Sebat et al., 2004; de Vries
25
et al., 2005; Sharp et al., 2005; Tuzun et al., 2005; Conrad et al., 2006; McCarroll et al., 2006).
An increasing number of chromosome abnormalities are reported as associated with various
phenotypes, suggesting an important role for CNVs in common diseases (Buckland, 2003).
CNVs can alter gene expression in different ways. Dosage sensitive genes, for example,
can be influenced by deletions and duplications and alter the phenotype of the carrier. It is, for
example, a priori, expected that duplicated a gene will show a ~1.5-fold increase in transcription.
It is likely, however, that, due to selective pressure, most common copy number variants will be
associated with more subtle phenotypic changes. In fact, in silico analyses of CNV have shown
that large deletions are significantly underrepresented in transcriptionnally active regions of the
genome (Conrad et al., 2006). The correlation between copy number and expression levels
appears to differ greatly at different loci. For example, copy number does not correlate with
variations in mRNA and protein levels of the α-defensin gene, suggesting the presence of
additional trans-acting elements (Aldred et al., 2005; McCarroll et al., 2006) but there is an
almost perfect correlation in the case of the α-synuclein gene (Miller et al., 2004).
Genes encoding nucleic acid binding proteins and nucleic acid metabolism appear to be
underrepresented in CNVs (Conrad et al., 2006), suggesting a dosage-sensitive nature for several
of these genes and reflecting their role in the regulation of transcription. Genes associated with
environmentally-regulated functions such as, immune response, drug metabolism, signal
transduction, etc, on the other hand, were found to be enriched in CNVs, suggesting an adaptive
advantage of dosage “imbalance” (Tuzun et al., 2005; Conrad et al., 2006; Nguyen et al., 2006).
In general, duplicated genes encode for longer proteins containing more functional domains and
more cis-acting elements (He & Zhang, 2005). Cross-species analysis indicates that genetic
divergence increases in duplicated genes and that the majority of changes occur in cis-regulatory
elements in the proximal promoter region (Kondrashov et al., 2002; Gu et al., 2005).
26
Duplications, deletions and inversions can also disrupt genes located at the breakpoints of
the rearrangements or their regulatory elements, leading to the creation of truncated, generally
non-functional, proteins or changes in transcription levels. Finally, deletions can reveal recessive
mutations present on the remaining allele.
1.3.6 Methods for the study of CNVs
Since the first identification of cytogenetic abnormalities, there has been an important
increase in the number and nature of CNVs identified on the human genome. Because the
resolution of early light microscopy-based karyotyping techniques is approximately 3-5 Mb, the
majority of CNVs remained undetected until relatively recently. The development of isotope
labeling techniques (Pardo et al., 1975) and of fluorescence in situ hybridization (FISH) (Pinkel
et al., 1986) allowed the localization of specific genomic regions but the nature of the methods
did not allow for them to be used as genome-wide screening tools.
The development of microarrays (Pinkel & Albertson, 2005), originally composed of
thousands of probes immobilized on a substrate, allowed, for the first time, the high-resolution
screening of entire genomes. This has led to the identification of several hundred CNVs on the
human genome. The recent development of arrays using cloned cDNAs, single stranded PCR
products and oligonucleotide arrays considerably increased the resolution of genomic arrays
(Pollack et al., 1999; Brennan et al., 2004; Dhami et al., 2005). Finally, computational methods
utilizing publicly available data such as paired-end mapping has further increased the capacity to
detect structural variants, including balanced abnormalities (Tuzun et al., 2005). See Table 1.1 for
a summary of current genome-wide CNV screening tools.
1.3.7 Chromosome abnormalities in ASD
A great variety of rearrangements, involving all chromosomes, has been reported in
individuals with ASD, microdeletions and microduplications being the most common
27
Table 1.1 Techniques available for genome-wide screening of CNVs
Technique
Advantages
Disadvantages
cDNA/BAC arrays
Screening of entire genomes
Lower resolution
Oligonucluotide arrays
High density custom design
Lower specificity of probes
SNP microarrays
Can determine parent of origin
Limited by presence of
SNPs (usually exclude
segmental duplications)
Slightly more costly –
although price is decreasing.
Paired-end mapping
Detection of balanced
rearrangements
Requires high-density
sequence data
Larger insertions are not
detected
Very costly
Comparative (cross-species)
sequence analysis
Detection of balanced
rearrangements
Requires sequence data
Sensitive to assembly errors
28
(Lauritsen et al., 1999). The majority, however, have been found in single cases and their
importance remains unknown (for a list of case reports, see Veenstra-Vanderweele et al., 2004;
Kakinuma & Sato, 2008). Yu et al. (2002) first demonstrated the relevance of CNV screening in
autism. In the course of performing a genome scan for autism-susceptibility using microsatellite
markers at ~10 cM intervals in 105 multiplex families, they identified four interstitial deletions,
two on chromosome 7 and two on chromosome 8, in individuals from 12 families. Most deletions
were relatively small (<200 kb) and one was also found in the control group of 40 families.
Although most of these deletions are now known to be small copy-number polymorphisms
(Iafrate et al., 2004), the unexpected identification of microdeletions in this linkage study paved
the way for further CNV-screening in ASDs.
Jacquemont et al. (2006) identified 33 CNVs, ranging in size from 200 kb to 16 Mb, in
22/29 patients with ASD using a 1 Mb resolution array-CGH. CNVs (8) in 8 patients were
ultimately considered as clinically relevant on the basis of their size, de novo occurrence, and
gene content. Pathogenic abnormalities were therefore identified in 28% of the investigated
patients thus supporting the hypothesis that array-CGH is an effective tool for identifying
candidate gene regions for ASDs.
The Autism Genome Project Consortium used a 10K SNP array to test 1168 heterogeneous
MPX families, broadly subgrouped across 3 categories (narrow, broad, and heterogeneous ASD),
for linkage and copy-number changes (Szatmari et al., 2007). Apparent de novo CNVs were
identified on 8 genomic regions in 10 families. Differentiation between potentially pathogenic
CNVs and common polymorphisms was difficult to determine for most of the rearrangements, as
limited phenotypic information was available for the large, heterogeneous group of families
tested
29
Initial estimates reported that genomic abnormalities were identified in 3% of patients with
an ASD (Reddy, 2005; Folstein & Rosen-Sheidley, 2001). Recent studies have reported that the
proportion of de novo CNVs differs between simplex families (7-10%), multiplex families (2-3%)
and healthy controls (1%) (Sebat et al., 2007; Marshall et al., 2008). Also, 27% of families with
syndromic forms of autism presented with de novo abnormalities (Jacquemont et al., 2006).
The most frequent abnormality in ASD are duplications of chromosome 15q11-q13, most
commonly in the form of a supernumerary isodicentric chromosome or, less frequently, from an
interstitial duplication (Folstein & Rosen-Sheidley, 2001). Deletions of the region lead to two
distinct neurodevelopmental disorders, PWS and AS, depending on whether the deletion is
paternally or maternally derived (Christian et al., 1999). These two disorders are associated with
autistic-like symptoms in a subset of cases (Maestrini et al., 2000). Duplications of 15q11-q13
associated with autistic behaviour were initially reported in 6 males with moderate to severe MR
and epilepsy (Gillberg et al., 1991). Cook et al. (1998) screened several microsatellite markers
across the 15q11-13 region and found duplications in two unrelated autistic individuals out of 140
probands tested. Estimates of prevalence for this genomic abnormality in ASD range from 0.5 to
3% (Browne et al., 1997; Cook, Jr. et al., 1998; Schroer et al., 1998; Sebat et al., 2007). Patients
with maternally derived duplications of the PWS/AS critical region commonly present with ASD
(Browne et al., 1997; Cook, Jr. et al., 1997; Mao et al., 2000; Bolton et al., 2001). Paternallyderived duplications, on the other hand, are generally associated with a normal phenotype but, in
a few individuals, can be associated with developmental delay and ASD (Mohandas et al., 1999;
Mao et al., 2000; Roberts et al., 2002). Both maternally and paternally-derived triplications are
associated with a more severe phenotype that includes developmental delay, language deficits,
epileptic seizures and, in some cases, ASD or autistic behaviours (Roberts et al., 2002; Vialard et
al., 2003; Dennis et al., 2006).
30
Other recurrent CNV identified in individuals with ASD include duplications and deletions
of 16p11.2, and deletions of 22q11-q13, each accounting for ~0.5%–1% of cases (Marshall et al.,
2008; Kumar et al., 2008; Weiss et al., 2008). The 16p11 rearrangements span ~500 kb and is
flanked by LCRs that share >99% sequence homology. A 16p11.2 duplication reciprocal to the
deletion has also been observed in ~0.5% of ASD patients but was also detected at similar
frequencies in controls (Marshall et al., 2008; Kumar et al., 2008). Kumar et al. (2009) performed
association studies using SNP arrays and did not find evidence for association of any marker
tested with ASD in their family set. They suggested that genomic CNVs represent a more
significant risk factor for ASD than point mutations at that locus.
The 22q13 deletion syndrome, which affects males and females equally, is associated with
developmental delay, speech impairments, facial dysmorphologies and autistic-like behaviour
(Manning et al., 2004). The majority of cases (~80%) occur de novo but children can also inherit
an unbalanced chromosome from a parent carrying a balanced reciprocal translocation. Typical
22q13 deletions range in size from 5 to 8 Mb with the critical area spanning approximately 100
kb and containing three genes: SH3 and multiple ankyrin repeat domains 3 (SHANK3), acrosin
(ACR) and RAB, member of RAS oncogene family-like 2B (RABL2B) (Anderlid et al., 2002).
Glessner et al. (2009) screened the genome of 2195 individuals with ASD, including 1336
from the Autism Genetic Resource Exchange (AGRE), and 3609 controls, for the presence of
CNVs. While similar CNV frequencies were observed in cases and controls (15.5 CNV calls per
individual on average), rearrangements on specific genomic regions were significantly associated
with ASD cases, including some with well-established associations with ASD such as 15q11-q13
and 22q11 duplications (15 and 9 cases, respectively). CNVs of the contactin 4 (CNTN4) gene on
chromosome 3p26.3, which have also recently been reported in two recent independent studies
31
(Fernandez et al., 2008; Roohi et al., 2009), were also observed in 19 cases from 14 families and
one control subject. All cases inherited the CNV from a healthy parent.
The authors observed similar frequencies (~0.3%) of genomic abnormalities at 16p11.2 as
previously reported (Weiss et al. 2008), but noted that the frequency was similar to that observed
in controls. As well, 16p11.2 CNVs did not segregate in all cases in three families and were
transmitted to an unaffected sibling in another.
In addition to those previously reported genomic regions, Glessner et al. (2009) identified
four CNVs observed exclusively in ASD cases as well as five others with significantly higher
frequencies than controls. Among these were located four genes (Ubiquitin protein ligase E3A;
UBE3A, Parkinson disease 2; PARK2, Ring finger and WD repeat domain 2; RFWD2, and F-box
protein 40; FBXO40) that belong to the ubiquitin gene family. Although each individual CNV
they identified was rare, their combined effect on ubiquitin degradation may contribute to ASD
susceptibility in a number of cases.
Bucan et al. (2009) performed high density genotyping in 1771 ASD cases and 2539
controls and found more than 150 rare variants present in multiple probands and absent from
controls, several of which were inherited from healthy parents. Among the most common CNVs
identified were those at the benzodiazapine receptor (peripheral) associated protein 1 (BZRAP1,
chromosomes 14q21.3, 8 cases) and MAM domain containing glycosylphosphatidylinositol
anchor 2 (MDGA2, 17q22, 6 cases) loci. BZRAP1 encodes an adaptor molecule that regulates
synaptic transmission by linking vesicular release to voltage-gated calcium channels (Wang et al.,
2000). The function of MDGA2 remains elusive.
Because of the diversity of loci implicated so far, a selection of cases is needed to identify
abnormalities of interest. The optimal situation occurs when a chromosomal aberration cosegregates with the autistic phenotype within a family. Alternatively, a de novo rearrangement in
32
an individual with autism with a negative family history for ASDs also provides a good starting
point for further analysis.
1.4 Hypothesis and objectives
Identifying the genes contributing to complex genetic disorders requires a variety of
complementary approaches. The specific nature of genetic variation contributing to ASDs is
unknown, but various models and strategies to identify the underlying genetic factors have been
proposed.
The CDCV model suggests a joint action of several common variants shared by unrelated
affected individuals in causing common disorders (Lander, 1996; Chakravarti, 1999). Under this
hypothesis, association studies using common SNPs could be used to map disease susceptibility
alleles (Risch & Merikangas, 1996). Because there are interdependent interactions of several loci,
the effect of any given variant will be more modest and linkage studies will be less effective
(Slager et al., 2000). The CVCD model can then be verified by carrying out association studies,
which assess the correlation between genetic variants and a disorder on a population scale in large
sample sizes. Association methods require a common mutational origin in the subjects and will
therefore not be helpful to find causative genes when the major cause of the disease is different
new mutations in the same gene.
However, rare alleles might be difficult to detect by association even if they have strong
effects. Indeed, tag SNP approaches are designed to tag common polymorphisms, with frequencies
greater than 5%. The best strategy for identifying genes under the rare variant – common disease
model would be to reduce genetic heterogeneity by choosing non-randomly mating populations
such as inbred, polygamous, culturally (eg: religion) or geographically isolated populations.
Subgrouping individuals based on certain key phenotypic traits can also be a way of filtering out
some genetic heterogeneity.
33
No single model or approach to identify culprit genes in complex disorders such as ASDs
will suffice and a combination of approaches is thus necessary. One of the major hurdles lies in
the designation of phenotypes. Broad classifications such as ASD are insufficient as these
disorders are highly heterogeneous. Additional phenotypic characterization of affected
individuals and their relatives according to a variety of clinical and behavioural properties could
allow the creation of subgroups in order to reduce heterogeneity. Also, the recruitment of large
numbers of families for association studies should allow the identification of genes of small to
medium effect.
The present study is aimed at identifying culprit genes for ASDs through the characterization
of novel microdeletions and microduplications identified in individuals with ASD using CGH
microarrays. The overall hypothesis is that microdeletions and microduplications found in
individuals with ASDs signal the locations of ASD-related culprit genes.
There are four specific objectives:
1. To localize and characterize genomic rearrangements in individuals with ASD
2. To screen for additional rearrangements amongst large populations of unaffected controls
and probands.
3. To identify candidate genes and test them for association with ASD.
4. To examine the presence of mutations in candidate genes.
1 Mb CGH-microarrays are ideal to identify candidate genomic regions. Array-CGH has
served as a key tool in identifying culprit genes for clinical syndromes associated with variable
dysmorphologies and ID. For instance, mutations in the RAI1 gene, which encodes a PHDcontaining protein, are now associated with the SMS 17p11 microdeletion syndrome (Bi et al.,
2004). De Vries et al. (2005) used a 0.1 Mb resolution microarray to screen for CNVs in 100
subjects with idiopathic ID and congenital anomalies. Ten percent of subjects had de novo
34
alterations ranging from 540 kb to 12 Mb in size. Of note, the majority of genomic abnormalities
(258/268) were inherited from a parent.
By testing individuals with more complex, syndromic forms of autism, the likelihood of
finding genomic abnormalities in affected individuals is increased (Takahashi et al., 2005). Thus,
individuals with complex ASD (i.e.: with physical anomalies +/- ID) were selected using criteria
established by de Vries et al. (2001)
In addition to identifying a narrower region than microsatellite-based genome scans (1 Mb to
5 Mb vs >20 Mb), genetic heterogeneity is no longer a concern as genomic rearrangements are
found in individuals, not in a group of families. This part of the work was carried out in the
laboratory of our collaborators, Dr ME Suzanne Lewis and Dr Evica Rajcan-Separovic at the
University of British Columbia. Sections 1.6, 1.7 and 1.8 describe the three genomic abnormalities
that were further characterized as part of this thesis. A brief clinical description of the three
probands carrying the CNVs is also presented.
Real-time semi-quantitative PCR (sqPCR) was used to confirm all rearrangements
identified and to determine whether they were inherited or occurred de novo. Approximately 200
controls are needed to test for the presence of the same rearrangement to ensure that it is not a
common copy-number polymorphism. A total of 798 individuals with ASD were tested to
identify further cases and to determine the frequency of the abnormality in these disorders. The
breakpoints of the rearrangements were refined using sqPCR in order to determine the
mechanism(s) by which the abnormality occurred and to identify the genes that were
deleted/duplicated. The most likely candidate genes for ASD were selected, based on their
function, expression pattern and potential relevance, and tested for association with ASD. For
genes found to be associated with ASD, sequencing was carried out to identify ASD-associated
mutations.
35
1.5 Patients with rearrangements.
1.5.1 Subject 1 with del(2)(p15-p16.1) and del(x)(p22.2-p22.3)
Subjects 1 is from a simplex family, originally identified and described by RajcanSeparovic et al. (2007). She is an 8-year-old nonverbal female, born to non-consanguineous, 23and 26-year-old Caucasian parents, with autistic disorder diagnosed by the highest measures
[ADOS-G (Lord et al., 2000) and ADI-R (Lord et al., 1994)]. She has dysmorphic features,
moderate to severe ID, and ADHD. Hearing is normal and there was no history of clinical
seizures. Visual problems include strabismus and optic nerve hypoplasia. Cranial magnetic
resonance imaging (MRI) indicated the presence of a cortical migration defect involving the
perisylvian brain region. She has a brother with a normal physical and neurodevelopmental
phenotype. Routine cytogenetic studies at amniocentesis and postnatally (450–500 band level)
were normal, as were FISH studies for Angelman and Miller-Dieker syndromes.
1.5.2 Subject 2 with del(2)(p15-p16.1)
Subject 2 is a 6-year-old, hearing impaired, nonverbal male with autistic features
subthreshold for a definitive ASD [ADOS-G Module 1 testing scores were: Communication (0;
ASD cutoff = 2); Reciprocal social interaction (4; cutoff = 4); Play (2; cutoff = 7); Stereotyped
behaviours and restricted interests (2; no cutoff value defined)] with a constellation of
dysmorphic features similar to those in Subject 1. Microcephaly was observed at birth and
progressive growth retardation, persistent microcephaly, moderate ID and global developmental
delay were documented, including mild visual impairment due to hyperopia and optic nerve
hypoplasia. He presented with severely delayed receptive and expressive language skills
confounded by sensorineural hearing loss (2000-6000 Hz), as well as deficits in social interaction
and attention. There was no seizure history; however, electroencephalogram testing revealed a
dysrhythmic background and frontal/occipital spike waves suggesting a heightened predisposition
36
to epilepsy. Dysmyelination and cortical dysplasia, in keeping with a cortical migration defect,
was evident on cranial MRI. Subject 2 does not have any siblings.
1.5.3 Subject 3 with dup(7)(q11.23)
Subject 3 is a 12-year-old female meeting DSM-IV criteria for an ASD and severe
expressive language deficiency confirmed by ADOS-G/ADI-R (Lord et al., 1994; Lord et al.,
2000) and multidisplinary assessments by a clinical geneticist, developmental pediatrician,
psychologist, child psychiatrist and child neurologist. Initially diagnosed at age 9 with an ASD,
verbal apraxia and anxiety features, it was felt that she did not meet additional criteria for selective
mutism as normal speech was not identified in any setting. Repeat developmental review at age 12
showed the persistence of severe verbal apraxia with communication via some gestures, pointing and
written communication consisting of single words. Social anxiety and limited interests were still
present. Adaptive skills were significantly low in social and problem solving domains with relative
strength in self-care. Repeat cognitive testing showed nonverbal skills in the borderline range and
verbal skills (testing completed using multiple choice formats of standard verbal subtests) in the
intellectually disabled range. Overall, the findings from the intellectual and adaptive assessment met
criteria for a mild ID (IQ between 50-70). Of note, like the cases described by Berg et al. (2007),
sparing of visual spatial abilities were seen with our case and these skills were all average. She
showed sensory avoidant behaviours stemming from infancy and social avoidance/ anxiety first
seen in the preschool years. Tics, repetitive motor mannerisms and/or self-stimulatory behaviours
were not present. Attention was good with compliant behaviour during testing.
Family, pregnancy, birth and systemic reviews were unremarkable. Neurological assessment
and investigations including cranial computed tomography and magnetic resonance imaging as
well as electroencephalogram testing was normal, as were hearing assessments performed on
three occasions. Other investigations included a normal 46,XX karyotype (>550 band resolution)
as well as negative Fragile X testing.
37
Growth parameters revealed height, weight and head circumference respectively at the 8th,
65th and 50th centiles. A detailed protocol of anthropometric craniofacial measures was performed
which revealed a flat occiput with brachycephaly, slight asymmetry to the face with right greater
than the left sided prominence to the angle of the jaw. The eyes were normally positioned and
ears normally formed, but low set. There was a broad and high nasal root with low hanging
columnella and long pointed nose. There was mild prognathism and a thin upper and lower lip
with short philtrum. The remainder of the systemic and morphological examination was negative.
The duplication found in Subject 3 was not present in her mother nor in her unaffected brother.
The father was not available for testing.
1.6 Deletions at 2p15-p16.1
1.6.1 Background
Our collaborators recently reported the identification of two unrelated individuals (Subjects
1 and 2, described above) with similar de novo interstitial deletions at 2p15-p16.1 (RajcanSeparovic et al., 2007). Both individuals presented with autism or strong autistic features, in
addition to moderate to severe ID, microcephaly, cortical migration defects, renal anomalies,
digital camptodactyly, visual and communication deficits, and a distinctive pattern of craniofacial
features.
Additional smaller overlapping rearrangements involving the described 2p15-16.1 region
have been reported in controls, including a recurrent 2.9 Mb deletion/duplication in normal
individuals (58.2-61.1 Mb; http://projects.tcag.ca/variation/) encompassing many genes such as
peroxisome biogenesis factor 13 (PEX13), REL, and Fanconi anemia, complementation group L
(FANCL). Another patient with a de novo duplication of band 2p16.1 (clones RP11-201J10 to
RP11-44OP5; ~57.1 Mb-60.7 Mb) was reported in the Decipher database
(https://enigma.sanger.ac.uk/perl/PostGenomics/decipher/; patient CHG00001375 and Dr Szuhai
38
K, personal communication). However, the patient did not resemble the two subjects studied here
(Rajcan-Separovic et al., 2007) and did not have an ASD. Additionally, recurring de novo
microdeletions of various extents have been detected within the described 2p15-16.1 region
(Chabchoub et al., 2008; de Leeuw et al., 2008), that were not reported in association with a
confirmed ASD. The latter patients did present, however, with several phenotypic traits
overlapping with those found in our subjects, including ID and facial dysmorphologies such as
high palate, smooth upper vermillion border, everted lower lip, broad and high nasal root, and
telecanthus. It was proposed by Chabchoub et al. (2008) that the common features may be
associated with five genes present within a 570 kb deletion. This smallest critical region
is common to all 4 subjects (Figure 1.2). The authors suggested that Exportin-1
(XPO1), KIAAA1841 and USP34 may be linked to the multiple anomalies observed in all patients
with a del(2)(p15-16.1) due to the expression of XPO1 in multiple tissues, brain specific
expression of KIAAA1841 and role of ubiquitins in ID syndromes and autistic disorders. Of note,
the OTX1 gene was not deleted in the two new cases.
1.6.2 OTX1 as a candidate gene for autism
I propose that while the overlapping 2p deletion region observed in subjects reported by
our collaborators (Rajcan-Separovic et al., 2007) and others (Chabchoub et al., 2008; de Leeuw et
al., 2008) (Figure 1.2) likely accounts for their common syndromic findings, potential autism
genes lie in the region discordant from that previouly defined. Of the 40 genes and hypothetical
proteins deleted in Subjects 1 and 2, the number of potential autism genes was narrowed down to
the 8 that were outside of the region of overlap with other, non-autistic, reported cases (Figure
1.2): hypothetical protein FLJ13305, chaperonin containing TCP1 subunit 4 (CCT4), copper
metabolism (Murr1) domain containing 1 (COMMD1), similar to 40S
39
Subject
Patient 11
Subject
Patient 22
Chabchoubetetal.
al.
Chabchoub
de Leeuw
Leeuw et
et al.
al.
De
Figure 1.2 Deletions on chromosome 2 in Subjects 1 and 2 compared with those reported by
Chabchoub et al. (2008) and de Leew et al. (2008) The BAC clones representing the deletions in
both Subjects 1 and 2 are represented with a black box and those balanced in both subjects are
indicated with an empty box. Bi-colored clones were found to be hemyzigous only in Subject 1.
40
ribosomal protein SA (p40) (LOC388954), UDP-GlcNAc:betaGal beta-1,3-Nacetylglucosaminyltransferase 1 (B3GNT1), transmembrane protein 17 (TMEM17), EH domain
binding protein 1 (EHBP1), and OTX1. I selected the OTX1 gene as being of greatest interest for
candidate gene testing.
OTX1 comprises a 1.07 kb open reading frame with three exons distributed over 3.4 kb of
genomic sequence which encodes a protein that acts as a transcription factor. The OTX1
homeodomain (structural domain that binds DNA) has a lysine at position 9 of the recognition
helix, an unusual feature amongst transcription factors, which provides OTX1 with a high affinity
for TAATCC/T sites on DNA (Hanes & Brent, 1989; Hanes & Brent, 1991). It is unclear whether
dimerization plays a role in recognition of target sites but OTX1 monomers are known to bind
with high affinity and specificity to their target sequence (Gan et al., 1995). The details of OTX1mediated activation of transcription remain vague and the proteins it interacts with, if any, are
still unknown.
OTX1 is involved sensory organ development: abnormalities are observed in the ciliary
process in the eye and the lateral semicircular duct in the inner ear in OTX1(-/-) mice (Acampora
et al., 1996; Acampora et al., 1999). As previously noted, sensory impairments are common in
individuals with autism. Leekam et al. (2007) tested 33 individuals with autism using the
Diagnostic Interview for Social and Communication Disorders (DISCO) (Leekam et al., 2002)
which uses 21 items grouped in 7 domains (auditory, visual, touch, smell/taste, etc), and found
that individuals with autism had a significantly higher mean score than those in the control groups
and that over 90% of all subjects presented with symptoms in more than one sensory domain.
OTX1(-/-) mice present with a smaller brain, both in size and weight with a reduction in
thickness of the dorsal telencephalic cortex and a shrunken hippocampus. A 40% reduction in the
number of cells in the temporal and perirhinal areas of the cortex is also observed (Acampora et
al., 1996). A defective proliferation of neuronal progenitors during early stages of development
41
may be responsible for this reduction (Acampora et al., 1998). In light of the cortical migration
defects present in both our 2p-deleted subjects as detected by MR neuroimaging, OTX1, given its
role in the organization of the neuronal and axonal layers, is an excellent candidate gene for ASD.
OTX1(-/-) mice exhibited spontaneous high speed turning behaviour, reminiscent of the
repetitive behaviour observed in ASD (Acampora et al., 1996). It is also interesting to note that
OTX1(-/-) knockout mice described by Acampora et al. (1996) manifest seizures, observed in 442% of ASD cases (Giovanardi et al., 2000); OTX1(+/-) mice, on the other hand, showed no signs
of epilepsy. Subjects 1 and 2 do not present with clinical epilepsy, although EEG testing
suggested a heightened predisposition to seizures in Subject 2. As only one of two copies of the
gene is presumed deleted based on the observed hemizygosity of the 2p15-16.1 region, it is likely
that hemizygosity of OTX1 does not have a sufficient effect on its expression to cause clinical
epilepsy.
1.7 Deletion at Xp22.2-p22.3
1.7.1 Background
The distal region of chromosome Xp has a high frequency of interstitial and terminal
deletions (Ballabio et al., 1989). Large deletions of the Xp22 region have been described in
patients affected with various diseases or disorders, including: Kallmann syndrome, ocular
albinism type I and X-linked recessive chondrodysplasia punctata. Several genes map to Xp22,
including STS, deficiency of which causes X-linked ichtyosis (XLI). The majority of patients with
XLI (~ 90%) have large deletions of the STS gene and flanking sequence.
1.7.2 NLGN4 and VCX as candidate genes for ASD
1.7.2.1 NLGN4
The neuroligins (NLGN) are cell adhesion molecules that through interactions with their
ligands, α- and β-neurexins, promote the formation of synapses (Song et al., 1999; Chih et al.,
42
2004; Varoqueaux et al., 2004). NLGNs are abundant in the post-synaptic membrane of
glutamatergic (NLGN1, 3 and 4) or GABAergic (neuroligin 2; NLGN2) synapses where they
associate with PDZ-domain scaffolding proteins to regulate the glutamate-GABA balance (Meyer
et al., 2004; Chih et al., 2005). NLGNs form homomultimers through an acetylcholinesterase
(AchE)-homologous domain, which plays a vital role for neuroligin function (Comoletti et al.,
2003). Alternative splicing occurring in the AchE-homologous domain of NLGNs is known to
alter ligand-binding affinity although the role of distinct splice variants in vivo remains to be
determined (Boucard et al., 2005; Chih et al., 2006).
NLGN3, located at Xq13 and NLGN4 at Xp22.32 are associated with the formation of
presynaptic elements in hippocampal neurons (Jamain et al., 2003; Chih et al., 2004; Laumonnier
et al., 2004). These genes were first reported to be associated with autism in two Swedish
families (Jamain et al., 2003). They identified a missence mutation (R451C) in NLGN3 within the
key AchE-homologous domain in the first family, and a basepair insertion causing a frameshift
mutation in NLGN4 in the other. The mutations segregated with ASD status and were absent from
controls. Cell culture studies showed that the two mutations increase intracellular retention of the
encoded proteins, which reduces or abolishes synaptogenesis (Chih et al., 2004; Chubykin et al.,
2005).
Laumonnier et al. (2004) subsequently reported a 2-base-pair deletion in NLGN4 in a large
French family. The deletion was present in all male family members affected by X-linked mental
retardation, autism or PDD but not in non-affected family members or controls. The mutation
caused a premature stop codon (429X), suppressing the transmembrane and dimerization
domains, which are required for cell-cell interactions.
Three missense mutations (G99S, K378R and V403M) in the AChE-homologous domain
and one (R704C) in the cytoplasmic domain were found by Yan et al. (2005) in NLGN4 in a
study of 148 unrelated autistic individuals. Individuals with mutations in the NLGN genes have
43
intelligence levels ranging from normal to severely impaired. Epileptic seizures were observed in
patients with mutations in NLGN3 but not NLGN4.
In addition to evidence associating NLGNs with ASDs, other recent studies have aimed at
finding mutations in other genes of the neurexin-neuroligin pathway. Neurexins are type-I
membrane proteins that mediate signaling across the synapse through interactions with NLGNs.
There are three neurexin genes on the human genome, each of which directs transcription of αand β-neurexins from independent promoters. Hundreds of neurexin isoforms are generated
through regionally-regulated alternative splicing (Tabuchi & Sudhof, 2002).
The role of neurexins in ASD susceptibility was first suggested by Feng et al. (2006) who
reported two missence mutations in neurexin 1 in four out of 203 patients with autism. In-frame
insertions and deletions were detected in 9 additional cases with no mutations identified in 535
healthy controls. Further evidence was reported by Kim et al. (2008) who identified mutations in
the neurexin 1 gene in two unrelated individuals with ASD. Additional studies have also reported
neurexin 1 deletions associated with ASD (Szatmari et al., 2007; Marshall et al., 2008; Yan et al.,
2008; Zahir et al., 2008).
Mutations in the SHANK3 gene have also been found in individuals with ASD. SHANK3
encodes an intracellular scaffolding protein that binds to NLGNs via DLG4 (discs, large homolog
4; also known as PSD-95) and guanylate kinase-associated protein (GKAP). Durand et al. (2007)
reported two de novo mutation, a 1 bp insertion and a 22q13 terminal deletion with the breakpoint
in intron 8 of SHANK3, in two unrelated individuals with ASD. A missense mutation (A962G)
was found in one of 400 Canadian patients with ASD screened by Moessner et al. (2007). The
authors also found a de novo deletion encompassing SHANK3 in an autistic female that was
absent in her two unaffected brothers. Gauthier et al. (2009) reported two SHANK3 mutations in
two of 427 individuals with ASD tested: a de novo deletion at an intronic donor splice site and a 1
bp missense substitution inherited from an epileptic father. Based on the presence of common,
44
recurrent breakpoints, Bonaglia et al. (2006) suggested that the autistic behaviours observed in
some patients with 22q13 deletion syndrome is likely to be due to alterations in SHANK3.
CNTNAP2 encodes a member of the neurexin superfamily that encodes a transmembrane
scaffolding protein (Poliak et al., 1999). CNTNAP2 was recently associated with ASD in a small
Amish community affected with several cases of cortical-dysplasia and focal epilepsy, associated
with autism in 67% of cases. A homozygous frameshift mutation in exon 22 of CNTNAP2 was
found in all 9 affected individuals. Four of the 105 healthy members of the community were
heterozygous carriers for the mutation and none were homozygous (Strauss et al., 2006). Other
recent studies provided further evidence for a role of CNTNAP2 in ASD. Arking et al. (2008)
performed genome-wide linkage and family-based association and found significant linkage at
7q35. The linkage peak included the CNTNAP2 locus. An overtransmission of the T allele of
marker rs7794745, located in intron 2 of CNTNAP2 was observed in a 72 multiplex families
study group and in a replication group made up of 1295 affected trios. Bakkaloglu et al. (2008)
sequenced CNTNAP2 in 635 patients and 942 controls and found 27 nonsynonymous changes, 13
of which were unique to affected individuals. Alarcon et al. (2008) also found a significant
association between variants in CNTNAP2 but they also identified a microdeletion in the gene in
a proband and his healthy father. The microdeletion was not found in 1000 controls.
There is accumulating evidence for the role of synaptic function genes in autism. The
mutations found in the neurexin-neuroligin complex in individuals with ASD suggest a role for
this pathway in the etiology these disorders.
1.7.2.2 VCX
Northern blots and reverse-transcriptase PCR (RT-PCR) experiments revealed that all
copies of Variable charge, X-linked (VCX) and Variable charge, Y-linked (VCY) genes are
45
transcribed in germ cells and in the testis (Lahn & Page, 2000). They are, thus far, the only
known human XY homologues that are all actively expressed in testis (Lahn et al., 2001).
Fukami et al. (2000) characterized Xp22 deletions in 15 patients with XLI. They found that
VCX3A was deleted in all patients with MR and preserved in those without MR. They found no
association between MR and the presence or absence of VCX, VCX2 and VCX3B. A deletion that
included the VCX3A promoter was also found in a patient with borderline MR with XLI (Hosomi
et al., 2007).
Mochel et al. (2008) reported a male patient with severe ichthyosis and Kallmann
syndrome who had inherited a 3.7 Mb interstitial Xp22.3 deletion from his mother. The patient
did not have MR and exhibited normal social habilities. A 7.7 Mb deletion of chromosome
Xp22.2-22.3 with a variable phenotype in female carriers was described by Chocholska et al.
(2006). The deletion encompassed 27 genes, including the four VCX genes and NLGN4. It was
transmitted from an asymptomatic grandmother to two daughters, one of whom had
developmental delay and autistic behaviour while the other was asymptomatic. The latter passed
on the deletion to her daughter and son. The daughter showed severe delays in psychomotor
development, hypotonia, microcephaly and ataxic gait. Several facial features were observed:
slight ptosis, hypertelorism, narrow palpebral fissures, broad nasal bridge and a curved upper lip.
She did not speak and did not maintain eye contact. The son also presented with altered
psychomotor development, his speech was delayed, and he showed autistic behaviours. Facial
dysmorphologies included a broad depressed nasal bridge, low set ears, and down-slanted corners
of the mouth. His hands were broad and short and clinodactyly of the fifth finger was observed.
The authors suggested that X-inactivation patterns might be responsible for the various
phenotypes in the females. Indeed, non-random inactivation of the paternal X-chromosome was
observed in lymphocytes of the severely affected granddaughter.
46
Taken together, the fact that VCX members have been implicated in MR and their deletion
in some patients with ASD or autistic behaviours, in addition to the fact that they are expressed in
germ cells makes them good candidate genes for ASD.
1.8 Duplications at 7q11.23
1.8.1 Background
Deletions of approximately 1.5 Mb at 7q11.23 are associated with Williams-Beuren
Syndrome (WBS), a neurodevelopmental disorder affecting 1/20,000 to 1/7500 newborns
(Stromme et al., 2002; Osborne & Mervis, 2007), and resulting from NAHR between flanking
LCRs (Urban et al., 1996; Stromme et al., 2002; Bayes et al., 2003; Osborne & Mervis, 2007).
WBS children are characterized by distinctive dysmorphic facial features (“elfin face”), low nasal
bridge, cardiovascular abnormalities, transient infantile hypercalcaemia, as well as growth and
developmental retardation (Francke, 1999; Jones et al., 2000). These children are hypersocial and
overly friendly, a phenotype that contrasts markedly with the deficits in social interaction skills
generally associated with autism (Jones et al., 2000). ASD traits, such as restricted interests and
hypersensitivity to sound, are also found in some individuals with WBS (Rosenhall et al., 1999;
Levitin et al., 2003; Laws & Bishop, 2004; Levitin et al., 2005; Brock, 2007); thus there is a
range of phenotypes, with the majority showing many of the generally-accepted WBS traits, but
with some overlap with autism spectrum traits as well. Some children with WBS and
del(7)(q11.23) have also been reported to have autism (Herguner & Motavalli, 2006; Jacquemont
et al., 2006; Edelmann et al., 2007).
Given that NAHR is responsible for generating the WBS deletion (Figure 1.3), the
reciprocal duplication is expected to occur at a similar frequency unless it is lethal. The first
individuals identified with a duplication of the WBS region at 7q11.23 were described by
Sommerville et al. (2005) and Kriek et al. (2006). In general, these individuals
47
HIP1
RFC2
EIF4H
CYLN2
GTF2i
WSCR1
STX1A
CPETR2
CPETR1
ELN
LIMK1
WS-bHLH
WS-βTRP
BCL7B
WSTF
FZD3
FKBP6
1.5–2.0 Mb
Cen
Tel
Figure 1.3 Williams Syndrome critical region on chromosome 7q11. Purple blocks illustrate
segmental duplications (duplicons) that act as substrates for unequal crossing-over events. Cen
and Tel give the orientations proximal to the centromere and distal to the telomere, respectively.
48
present with severe language impairment, developmental delay, mild dysmorphism, poor social
skills and, in some cases, epilepsy.
Berg et al. (2007) reported dup(7)(q11.23) in 7 individuals, two of which exhibited autistic
behaviours, with varying cognitive levels, severe expressive language delay and anxiety.
Depienne et al. (2007) subsequently screened 206 individuals with a definitive ASD diagnosis
and reported the first case of the duplication in an individual with autism. Until then, most
individuals screened for dup(7)(q11.23) were part of cohorts with developmental and language
delays. There have since been 24 additional dup(7)(q11.23) cases reported, at least 10 of which
presented with ASD or ASD-like behaviours (Berg et al., 2007; Kirchhoff et al., 2007; Torniero
et al., 2007; Torniero et al., 2008; Van der Aa et al., 2009).
1.8.2 STX1A, CYLN2 and GTF2i as candidate genes for ASD susceptibility
Three genes in this region, syntaxin 1A (STX1A), cytoplasmic linker 2 (CYLN2) and
general transcription factor II, i (GTF2i), were selected for association testing, based on their
involvement in neurotransmitter release, brain development and functioning (Sudhof, 2000;
Schuyler & Pellman, 2001; Edelman et al., 2007), and tested for association with ASDs.
1.8.2.1 STX1A
STX1A encodes a neuronal protein which has a membrane insertion domain near the Cterminal and a four-helix bundle that can undergo conformational changes (Misura et al., 2000).
It is involved in vesicle trafficking, docking and fusion as well as neurotransmitter release
(Sudhof, 2000). Vesicular fusion is mainly mediated by a group of SNARE (Soluble NSF
Attachment Protein Receptors) proteins, which include the synaptic vesicle protein
synaptobrevin-2 (or vesicle-associated membrane protein 2; VAMP2), and plasma membrane
proteins STX1A and SNAP25 (25kDa synaptosome-associated protein). Together, they fuse
49
synaptic vesicles at the presynaptic plasma membrane, triggering the release of their contents into
the synaptic cleft (Wojcik & Brose, 2007; Rizo & Rosenmund, 2008).
Yu et al. (2006) found that STX1A plays a crucial role in excitatory amino-acid carrier 1
(encoded by EAAC1) internalization and, consequently, in glutamate transport and conversion to
gamma-aminobutyric acid (GABA). Alterations of the GABAergic system have been suggested
to be associated with ASDs, with several studies reporting abnormal glutamate levels in the
plasma of individuals with autism (reviewed by McDougle et al., 2005). Moreover, given that
alterations in the expression levels of EAAC1 is a known causative factor of epilepsy (Gorter et
al., 2002; Simantov et al., 1999), Yu et al. (2006) suggested a potential functional link between
STX1A and epilepsy, which is observed in 4-42% of individuals with autism (Giovanardi et al.,
2000).
In addition to its role in exocytosis, STX1A is known to reduce calcium channel
availability thus inhibiting their activity (Bezprozvanny et al., 1995; Bezprozvanny et al., 2000;
Jarvis & Zamponi, 2001). Also, Haase et al. (2001) showed, using using 5-HT uptake assays,
confocal microscopy and co-immunoprecipitation assays, that STX1A interacts with 5-HTT to
alter its subcellular localization, resulting in a reduction of 5-HT transport.
The STX1A gene is present in the region of overlap between the duplication reciprocal to
the WBS deletion and the deletion reported by Jacquemont et al. (2006).
1.8.2.2 CYLN2
CYLN2 encodes CAP-GLY domain containing linker protein 2 (CLIP2), a member of the
family of cytoplasmic linker proteins expressed in dendrites and cell bodies of several neurons in
the brain (De Zeeuw et al., 1997). It is highly enriched in a specialized type of membrane
structure called the dendritic lamellar body (DLB), an organelle found exclusively in neurons
connected by dendrodendritic junctions (De Zeeuw et al., 1997). DLBs are present in neurons of
the hippocampus, piriform cortex, olfactory bulb, and inferior olive, all of which are areas of the
50
brain in which CLIP2 is detected. The inferior olive and the hippocampus have both been found
to by structurally altered in autistic brains. Structural magnetic resonance imaging (sMRI) studies
have revealed abnormalities of the hippocampus (Aylward et al., 1999; Howard et al., 2000;
Schumann et al., 2004). These findings cannot always be replicated, possibly due to different
image processing methods between research sites and differences between the groups tested. A
change in the distribution of neurons within the inferior olive was observed in individuals with
autism; neurons were found to cluster at the periphery of the structure in autistic brains (Bauman
& Kemper, 1985; Kemper & Bauman, 1993). Bailey et al. (1998) observed additional structural
alterations of the inferior olive in individuals with autism: discontinuities and thinning of the
neuropil, duplication of subnuclei, and ectopic clusters of neurons.
CYLN2 is also expressed in the Bergmann glia fibers in the cerebellar cortex. These cells
interact with Purkinje cells during processes such as long-term depression (Shibuki et al., 1996).
The number and size of Purkinje cells have consistently been reported to be reduced in the brain
of autistic individuals (Ritvo et al., 1986; Kemper & Bauman, 1993; Fatemi et al., 2002).
CLIP2 has been proposed to be involved in the interaction between membranous organelles
and microtubules, and alterations of CYLN2 expression may lead to defects in neuronal structure
and synaptic plasticity, as well as in brain development (Schuyler & Pellman, 2001). Hoogenraad
et al. (2002) found that CYLN2 (+/-) mice presented with brain abnormalities, hippocampal
dysfunction, deficits in motor coordination and mild growth deficiencies. They suggested that
haploinsufficiency for CYLN2 may underlie some of the developmental, neurological and
behavioral abnormalities observed in WBS. This is supported by studies of individuals with small
deletions in the WBS region. Studies on mutant mice by van Hagen et al. (2007) also suggested
that the motor and cognitive difficulties observed in WBS patients were linked to CYLN2 and
GTF2i; thus prospective candidate genes for ASDs.
51
1.8.2.3 GTF2i
GTF2i encodes an ubiquitously-expressed multifunctional phosphoprotein, general
transcription factor II-i (TFII-I), that can act both as an activator and a repressor of gene
expression (Perez Jurado et al., 1996; Hakimi et al., 2003). Several pathways are regulated by the
different isoforms of the TFII-I transcription factor and disruption of one or more of these
pathways could lead to autism or an autism-related endophenotype.
Edelmann et al. (2007) reported an atypical deletion of the WBS region in a female who
met diagnostic criteria for ASD and lacked the physical features of WBS (Edelmann et al., 2007).
GTF2i is located in the short region of overlap between the deletion in the subject described by
Edelmann et al. (2007) and duplications in persons with ASD or autistic behaviours reported by
others (Herguner & Motavalli, 2006; Berg et al., 2007; Depienne et al., 2007) and therefore
represents a good candidate gene for ASD.
52
CHAPTER 2
Materials and Methods
2.1 Clinical description
2.1.1 Subjects with ASD and control group
There were 798 affected individuals from 509 families available for association testing and
real-time PCR screening. Of 265 multiplex (MPX) families, 37 were reported by Robinson et al.
(2001) and 139 were obtained through AGRE (Los Angeles, CA) (Geschwind et al., 2001). The
remaining 89 MPX families, and 244 simplex (SPX) families, were recruited by the Autism
Spectrum Disorders – Canadian American Research consortium (ASD-CARC). DNA was
extracted from blood and/or saliva samples using QIAamp® DNA Blood Mini Kit (Quiagen,
Hilden, Germany) using standard conditions. Data from the Autism Diagnostic Interview-Revised
(ADI-R) (Lord et al., 1994) and/or PDD Behavior Inventory (PDDBI), which has been shown to
have excellent reliability with the ADI-R (Cohen, 2003; Cohen et al., 2003b), was available for
all cases. Details of the cohorts are summarized in Table 2.1
“Autism”, as defined in the AGRE pedigree algorithms
(http://www.agre.org/agrecatalog/algorithm.cfm), refers to individuals whose scores met or
exceeded specified cutoffs in all three core domains as established in the ADI-R algorithm, and
who had an apparent age-of-onset before 36 months. Those referred to as “not quite autism” met
the age-of-onset criteria and were no more than one point away from meeting autism criteria for
at least one of the three core domains or they met or exceeded cutoffs for all three domains but
did not meet the age-of-onset criterion. “Broad spectrum” generally includes individuals with
PDD-NOS or Asperger syndrome, who showed patterns of impairment along the spectrum of
PDDs. For the MPX families, at least one of the affected siblings had a diagnosis of autistic
disorder.
53
Table 2.1 Diagnostic details for individuals with ASD.
Diagnostic method
n
ADI-R
Autism1
Not quite autism
Broad spectrum
PDDBI
ADI-R, ADOS, Multidisciplinary Team assessment2
1
628
491
55
82
52
118
As defined in the AGRE pedigree algorithms (http://www.agre.org/agrecatalog/algorithm.cfm).
Individuals with “autism” met all criteria for autism; those labeled “not quite autism” were no
more than one point away from meeting autism criteria for at least one of the three core domains;
and those labeled “broad spectrum” were mildly to severely impaired, generally with PDD-NOS
or Asperger syndrome.
2
Referred by clinical geneticists following confirmation of an ASD diagnosis using gold-standard
ADI-R, ADOS, and DSM-IV measures.
54
The controls included DNA samples from 73 anonymous placentae, 101 individuals with
no personal history of autism, and 12 unaffected spouses from families with X-linked mental
retardation syndromes for a total of 186 individuals. Although information regarding psychiatric
and behavioural disorders was not available for the control group, the prevalence of ASDs is
unlikely to be greater than that in the general population.
2.2 Molecular analysis
2.2.1 Real-time semi-quantitative PCR
Real-time sqPCR was used to determine copy numbers of markers within genomic regions,
both for breakpoint determination and to screen for additional cases of rearrangements. The
breakpoints were refined using non-polymorphic markers located between deleted/duplicated
clones and the nearest balanced clones on each side of the rearrangement.
Oligonucleotides were designed with the PrimerExpress 3.0 program (Applied Biosystems,
Foster City, CA) using default parameters and sequence data from the NCBI Map Viewer (build
35.1; http://www.ncbi.nlm.nih.gov/mapview/). Primer and amplicon sequences were aligned with
the human genome using NCBI’s Basic Local Alignment Search Tool (BLAST;
http://blast.ncbi.nlm.nih.gov/Blast.cgi) to ensure specificity.
All PCR reactions were carried out in 384-well plates with concentrations of 900 nM for
each primer, 2 µL (5 ng/µL) DNA and 1X SybrGreen Mastermix (Applied Biosystems) in a 16
µL reaction volume. Amplifications were performed on an ABI Prism 7900HT (Applied
Biosystems) with the following amplification conditions: 50°C for 2 minutes, 95°C for 10
minutes, 50 cycles of 95°C for 15 seconds, and 60°C for 1 minute.
For each sample, the SDS2.0 software (Applied Biosystems) was used to determine cycle at
threshold, Ct, and a standard curve was generated to determine absolute quantities. Each locus
55
Table 2.2 Markers used for screening for cases of 2p16, 7q11 and Xp22 abnormalities in
probands and controls.
Probe
Primer sequences
2pScreen1
GGC TTT GCT CCT TCT CCT AGT TT
Position on chromosome (kb)
56 096
CCT TCC TCA GCC GCT TCT G
2pScreen2
CAT TCG CAG TTC CAA AAT GTG TA
58 488
GGC ATC TGA ACT GCC CAA A
2pScreen3
GAA ACA AGA AGC TTG GCT CAA AA
59 225
ATA TAC CCG CTG ACC CAC ATG
2pScreen4
AGG CAC AAA GAA GGA CCT ATA AGG
60 160
GAA GGC CTC TGC TGT GTG TCT
2pScreen5
CAG GCA ATA CAA ACC CAC TGA GT
60 889
GTG GGA TAC CTT GCG AAT TAG AA
2pScreen6
TTA AAC TGA GGA GCC AGG GTA TCT
62 112
AAC AGG CCC CCA GAG AAA AG
7qScreen1
GCC ACA GAG CAC TGC TGA AG
73 113
GAA AAA GCC TGC CCT CCA A
7qScreen2
CTG CTC TTC CTC CTG TGG CT
73 396
AGC TGT TTG CCC AGG TCC T
XpScreen1 TTTAAATGACTATCACAGGACCAGAGTT
6 576
CGGGCCCTATGTGATGTTGT
XpScreen2 GCACCACCTCCCTTTTTGTG
7 104
TGGAGAGGCAAGGTAGGAACA
XpScreen3 CCATGCCACAGTGAAGACTTGT
7 686
TTTGTGGTACACTGGCCCAAT
56
Table 2.3 Proximal Xp22 breakpoint refinement in Subject 1.
Probe
Primer sequences
XpProximal1
CCCGGGCCCCTTCAG
Position of
chromosome (kb)
7 606
CCCATCACTTCCAAACTTGGTT
XpProximal2
CAAACCAGAGGCCACTGGAA
7 636
GGTTGCAATGGGCCTTCTT
XpProximal3
GGTAATGGGTGAAGGAGATCCA
7 730
GCAGGGTTTTAACTTTTCCCTCAT
XpProximal4
TCTCGGAGGCTTAGGATGCTT
7 760
CCATTTCAGCCCCTTGGA
XpProximal5
AAATCACAGACCCAGGCAAGTAA
7 870
CTCTTTCAACCCAGGGAATTGT
XpProximal6
CCAGACCTTGTTTCTGTCATCTTTT
7 903
TCTGCAATTGCTGCTTTCTCA
XpProximal7
GCTCCTTTTTCTCCACCAATCA
8 004
TTTGGGTGCAGGAGGAAGAG
XpProximal8
CATGTGGATTGTGGCCACTAAA
8 156
AGGGCGTGCCTGATGTTAAC
XpProximal9
GCACAGGTGAGAACATGCTGTAA
8 172
CACACACTGTTTTTTCCGTCAAA
XpProximal10
CCAGCCTCTATGCAAGTGGAA
8 280
CAAAAGAGAACTCCAGCCTGATC
XpProximal11
CCACCGTGGGCATCGA
8 311
GCCTTCAGTGTGCAACTTGTG
XpProximal12
CCCTGCCCATGATGAGAGTAG
8 401
TCTGCCTCCCTGGCTTAGTG
XpProximal13
TGGCCCTTTACCTCCCAAAC
8 451
TGGGAAGGCCATCTGCTTT
XpProximal14
CGCGGACCAAAAAATGTATTTC
8 554
TGCCGGGCGCTGTAAC
57
XpProximal15
CCCCTGACTCAAAATACCCATAAA
8 689
CCTTAACCGTGTTCCTGCTTCT
XpProximal16
CTTGAAAGTGACTCGCAATTGG
8 872
GGCCTTAGAGTCCTGATAAATTGTG
XpProximal17
CGAAAAAGCCACAACTCACAGA
8 969
GGACTCATAAAAATGCCACTTAGCA
58
Table 2.4 Proximal Xp22 breakpoint refinement in Subject 1.
Probe
Sequence
XpDistal1
AAACACAGGCAAATGATGAGATATG
Position on
chromosome (kb)
6 005
AGCAACTGCATGAAAAGCAAAG
XpDistal2
AAAAGCTCCTTCTCCTTGGAAGA
6 067
GGACGGAAACGATAGAGTCAACTT
XpDistal3
AAAAAGCAAGAAACCAAGGTGAA
6 129
CATCCTCCACCCAACCTGAGT
XpDistal4
AAAAAGGCAAGCCCGAGACT
6 207
GGTTGGCGGGATGAGAACT
XpDistal5
TTTGTGTGAGAATTCCTGCTTTG
6 266
TTGCCATGGGCATGCA
XpDistal6
GGAAAACTGGAGTCATTGAAGGA
6 344
GCAAAAAATTACCTACCCCTGATC
XpDistal7
GTTTTCCAAAGAAAGTGAGCAATTT
6 403
TCAAGACGCTTCAGGCATCA
XpDistal8
TGTTCCCAGAGGATGAAATCAA
6 479
ACGCATGGCTTTGCATCTC
XpDistal9
GGTTGTGTGAAAGGCAAGCTTATT
6 542
CCCCAGGGTGAGAGCAGAT
XpDistal10
CCACATAACCATGGCCATCA
6 614
TCCGCAAAACTCATTAAAAAACG
XpDistal11
GGTTTCAGTACACGGGCAGTGT
6 675
TGCTTCCCAGCTCATGAAATC
XpDistal12
CAGCCAGCATTTACGATCAAGT
6 747
GGCAGGTGATAGAAGTCAAAGGA
XpDistal13
ATTGCCTGCAGCTCTCCAA
6 813
GAATGCCCACGTTGTACCTGTT
XpDistal14
CCATGACTTGTGGCTGCAGAT
6 876
ACTAGGAGAATGGGCCTCACTCT
59
XpDistal15
CAAACATGAGTCCTGAGAAGAAACC
6 983
GTGTGGTGCAGTTGCCATCT
XpDistal16
TGTGCAAATGGTGCAGAAAGA
7 033
CATGGGTACGAGCATCGTGAT
60
Table 2.5 Proximal 7q11 breakpoint refinement in Subject 3. Markers used for fine refinement are
italicized and were tested following larger scale refinement.
Probe
Primer sequences
7qProximal-A
AGGAGTAGCACAAAGTCCTGCAT
Position on
chromosome (kb)
71 674
TCGGAGGCTGCCCTAAGG
7qProximal-B
TTGGCCATATGCACAAGTAACTCT
71 724
CCAGGTGCAGACAGGATGCT
7qProximal-C
TCTTCCATCGCAGCAATAACAT
71 809
TGCATTCAGCTTTGCAGTCCTA
7qProximal-D
GGATCCTGTGCCCTATATGATTTC
71 872
CCAACTGCTCCACAGTAAACCA
7qProximal-E
AACAGCAAAGCCCAGATAAATGTAA
71 935
TGGGACCTCTCCTCACCTTTAA
7qProximal-F
CCACTCCTTCGATGAAACTCAGT
72 002
GAGCAGAAACCAGGGCAGAA
7qProximal-G
CTGCACAGTAAGCGGTTTGTG
72 048
TTGGTTGCTTGGATGGTGTTT
7qProximal-H
TGGGAGGGAATGAAACCAGAT
72 102
AAAGTCCCCATTCTTTTCCTTTTT
7qProximal-I
GCCCAGGTGCAGAGAGAGATA
72 187
AGGCTGCAGCAGGACAAGA
7qProximal-J
CCTTCAAAGCCCCCAGCTA
72 253
AAACTCGGCAGAGTCTAAGATCCTT
7qProximal-K
CTCTCGGTTCCGACGACAA
72 332
GGCAGAAGTGGCCTGCAT
7qProximal-K1
TGGGAGGGAATGAAACCAGAT
72 335
AAAGTCCCCATTCTTTTCCTTTTT
7qProximal-K2
GTGCTCTGGCCTTTCTTGCT
72 344
AGACTGCTCACGGCAATAACAG
61
7qProximal-K3
GGTAGCAGCTGCCCTGATGA
72 350
CCGCACCAATGGAAAATGA
7qProximal-K4
CGGTCGGCATCGAAGAAG
72 365
CCGCATCGGGCTCTACCT
7qProximal-K5
AGAGCCCCCGTCGGTTT
72 374
CACGATCCAAGCCAAAAATTC
7qProximal-K6
TGGGTCTGCGGCTCTGA
72 381
CCGCTCGTACAGGGACTGAT
7qProximal-L
CCGACCCTGTCACCCAATAT
72 385
GCTTTGAGTGAAATGGGAGGTT
7qProximal-M
TGCCACAGTGTCCGGAAA
72 431
CTGCCCAGCAAGTGAACAAA
7qProximal-N
CACCCAATGACCAAGAAGCA
72 512
CCCCTTCTGTGGTTTTCACACT
7qProximal-O
CGCACTAGAGGGCAGTCTGAA
72 575
GCCTATCACGGGCGCTTA
7qProximal-P
GCCTCATTTCCTTCCCTTTTG
72 634
CACTGCCCACAGCTGAGTCA
7qProximal-Q
CCCAAATGGCCCTAGAAGGA
72 690
GCGTGTTCCCCAACAGAGA
62
Table 2.6 Distal 7q11 breakpoint refinement in Subject 3. Markers used for fine refinement are
italicized and were tested following larger scale refinement.
Probe
Primer sequences
7qDistal-A
CTGGCAGAGAAGACTGACCAAGT
Position on
chromosome (kb)
73 355
GGTGCCCGGCTGTCTGT
7qDistal-B
TGTACCCCGTGAGTGTTCCA
73 380
CCCACCTCGGGCACTTC
7qDistal-C
GTATGGCCATGGGTGTGTCA
73 409
CGGAGCTGATGGAGGAACTG
7qDistal-D
CACGCTGTGCACACACTAACC
73 433
GGCCTCAGTTTCCCATCCAT
7qDistal-E
CTGGGTCTGTGGTTCAGCAA
73 465
GTGAGAAAACAGGAACAGATTGGA
7qDistal-F
CAGAGAAGGTGATAGGCAGAAAATG
73 487
CCACCTGCCCTGTGTACATCT
7qDistal-G
TGGTGAAGCCACTGTTGCA
73 515
GCCAGCCCTGTTCCGTTT
7qDistal-H
TCAGAATATTGTGCCTCCTCAAAC
73 540
GCTGAGGGCTCCATCTCTAGTC
7qDistal-I
TCCTGTGCCTGTCACTGAGTAAG
73 586
TCGTTTATCCTGAGGGCTTTTG
7qDistal-J
AAGCTGGTGCCAACTCTCATG
73 642
TGTTGGCGTCATCTTCATCTACA
7qDistal-K
ACAGCAGGCTGGTATGAATGAA
73 702
TGACCACACTCTGGCTTGTACCT
7qDistal-L
CCAGGAAGGGCGTTTGAA
73 753
GAAATGGCTAGACTTCCCTAAAGGA
7qDistal-L1
AACATGCCCTCTTTGACTTGGTA
73 758
63
GACCAATGCCTCTGGCTTTTC
7qDistal-L2
TCTCTTCTCTCCTCCCTCCAAGA
73 765
GAGCCCAAAGAAAGGCTTCA
7qDistal-L3
GGCTCACCGCCTTGCA
73 770
ATGTCCTAAAAACGCAGTCTTCAA
7qDistal-L4
GGAGTGCTAAGAACCGTGTAAAGG
73 775
TGGGAAGCTAGGCCCTAAGAG
7qDistal-L5
CAGGTGTCCCTTCCATTCCA
73 778
AAGCTGCTAGAGTAGAATGACATGAGA
7qDistal-L6
CACTGCATTAGGATCATTCTCTACATC
73 799
CAACACCAGTACGGCAAACTCTT
7qDistal-L7
TCATTTCGCCCATGTTCTGTT
73 802
CCAAGAGCTTCCCCTGCAA
7qDistal-L8
GGGTCTCTTCAGGGCCTCTT
73 804
CCCGGATGAGCTGAGCAA
7qDistal-M
CCTTCAAAGCCCCCAGCTA
73 806
AAACTCGGCAGAGTCTAAGATCCTT
7qDistal-N
TGAAAACCATACTTGGCAATGG
73 861
AAGCCGAGGTGAAAATCGAA
7qDistal-O
TCCAGCTCTTTATTGGCCAAA
73 921
CCACCAATTCTGTCTCCTATGAAA
7qDistal-P
GGATGGCTGTTTTAATGATGAGAGA
73 975
CGAAAGCTGGGTTTCCCTATC
7qDistal-Q
TTCCTGGGCCGGATCTG
74 025
GACCCAGTGCAGGCCAAA
7qDistal-R
CCAGCAAAGATCTGGGCATT
74 081
GGGCGGTCCTGGAGATG
64
Table 2.7 Proximal 2p16 breakpoint refinement in Subjects 1 and 2.
Probe
Primers sequences
2pProximal1
CCC CGT GGA GCA CAA GTG
Position on
chromosome (kb)
61 520
GGT CAG CAA GTC TGT TCT CTT AGA AA
2pProximal2
CCA CAA CTG CAC CTC TGT AAG G
61 741
TGC AGT GAT CTG TGG CAC AA
2pProximal3
ACA GCC CTC AAG CGT CAG A
61 968
CAA AAT GCC GAG GAT TGC A
2pProximal4
ATG TTT GCC CAG CCT GAA CT
62 188
AAA TAT TAA GCA GGG CCT TGC A
2pProximal5
TCT TCC CTG CCT CCT ATA CCA GTA
62 415
GCA GGT ATG AGC AGG AGA ACC T
2pProximal6
TCA TGG CCC TCT GAT AAT AGC A
62 635
TCA AGG GCC CAC ACA ATA CA
2pProximal7
CTG GAT GCC ACA CTA GAC ACA TC
62 878
GGA GTA GCT TTC TGC CAA TCA AC
2pProximal8
ACC CCA AAG CCA ACC AAT C
63 104
CAA GTG GTG CTA TTA ATT CAG AGT GAA
2pProximal8-A
GGG AAT TGA GGG CAA TGC T
63 119
TCA CAT ACC TCA CCC CAC CAT
2pProximal8-B
TGT GCC TCT TTT ACC TCC TTT GA
63 142
CAT TGG AAA GGA ATG CAA TTT TG
2pProximal8-B
GAC CAA ATC AGT GGT GCA TGA
63 173
AGC TCT TCT TCA GGC ATT TGT ACA
2pProximal8-D
AGG GCC TGT GTG CCA ATG
63 199
AAG GGA CTC TGT CCT CCA AAA AG
2pProximal8-E
TGG CTT GTA GGT ATT ATC AGT TTG AAG
63 228
TCA GCA ACA GAA AGA CAT TAA TTG G
2pProximal8-F
TCT GTG GGT TGG GCA AGA G
63 255
65
GGC CTG TCT TTA GCA CCT CAA
2pProximal8-G
TTC TTT TTC CTT TTC CCC CAT T
63 289
GAA ACA GTT CAG AGT AGA GCC CAA A
2pProximal8-H
CAT GCA AAG GTG TTA CAG TGA TTG
63 308
CTT ACA TAG CAC AGT GCA GCA AAG T
2pProximal9
CAA ACA AAT AAT ATA ACG CAC CCT GTA
63 322
GAA ACT GCG GCC AAC TCT TT
2pProximal9-A
GAA ATG ACG CAG GGC TCA AA
63 338
TGG CCA GCG GAT AAT TCC
2pProximal9-B
AAT AAC ACG CTC AGG ACA AGC A
63 364
TGT TAC AGA TTT TGA AGC ATT TTG G
2pProximal9-C
TGA CCA GTT TCA CGA AAG CAA A
63 393
ACA CAG GGT GGG AAA GCA TTA
2pProximal9-D
GAG TAA AAA GAT AAG GCC CAA CAA AG
63 429
TCT CCA GAG GCA TAT CCT TGG T
2pProximal9-F
GCA AAT GTG CTA GAG ATA GGA AGA GA
63 480
TGG GCC TAC TCA CAA CAG CTT
2pProximal9-G
CTG CTT GGT AAG CCA ATT TTC TAC T
63 511
AAG CCC ACA TAC TGA ACC AAA TG
2pProximal9-H
TCT CAT CCC TGT ATC CCC TTA CC
63 542
CAG GTG GGA AAC TGG AAA CTG
2pProximal10
AGG TGA TTT GGG AGG CTG TCT
63 556
GGA AGT ATG GAA CCG CAG TGA
2pProximal11
AAG GCA GAG CAG GAC CTT CA
63 777
GGG TCC CTG GAG CCT CAT
2pProximal12
GCT GCA TTT CTG GTT CTT GCA
63 881
GAC ATC ATA ATA GGA GAG ACA GGT CTG A
Markers used for fine refinement are italicized and were tested following larger scale refinement.
66
Table 2.8 Distal 2p16 breakpoint refinement in Subject 2.
Probe
Primer sequences
2pDistal201
CTG AAT GCC CTG AAT TAT TTG TTG
Position on
chromosome (kb)
55 753
AGC AGG ACT TTT GCC ATG CT
2pDistal202
TTA ATA GGC ACA CAG AAA ACT CAC AGT
55 725
GAG AAA TGA GAG AAA GAC CAG GAG AA
2pDistal203
CAG AAT GAA ATC CAA AAC CCT TTA TC
55 688
CAT TGA TTG TAG CCA AAA TGG AAA
2pDistal204
TGT AAT CCA TGT TGC CAT AAC CA
55 663
GCT CAG CAT TCA AAC ATT TCC TT
2pDistal205
GCG AGC ATT GAA GGA CCA AT
55 624
CAG TAC CTT GTC ATT TTG CTT ATA GTT CTT
2pDistal206
ACT CTT GCC CCA GTA CAT CCA
55 597
AAA TAA ACT CAG AAG AAC AGC TTG CA
2pDistal207
CCC CCA GTG GTT CTG GAT T
55 564
GCA ACA GCA CGT GGG AAG T
2pDistal208
AAC CTT CCA AGG GCC ACA TAC
55 534
AAG GAA ACC TGG ACC CAT GAT
2pDistal209
GCC AGT GAA TTT GAA GAA CCA AA
55 505
GCT CTC TTG GAA GCT ATT TAT TTT ATG AC
2pDistal210
GGA TCC TCC ATC CAT ACT CTG AGA
55 471
TTG GGA GTG GTG GAA AGC A
67
Table 2.9 Distal 2p16 breakpoint refinement in Subject 1.
Probe
Primer sequences
2pDistal101
CTG CTC CTT GTA AGC ACA TTT AGG
Position on
chromosome (kb)
56 992
CTG GAA CCA GAG GAT CGC TAG T
2pDistal102
CTG GAA GGG CCA CCA TCA
56 923
CTC AAC CCC TGT AGT GTA ACT CCA T
2pDistal103
CTC TCA ATG AAC AAG AGG AAG GAA T
56 866
TGC CCA ATT TTT CCA TTT TTC T
2pDistal104
CTG CTG TGC CAC CAA CCA
56 818
AAG TAC GGG TTC CTT AAG AGA GAT CA
2pDistal104-A
CGT CTC TGT GCA ATC CTG TCA
56 813
GAG ATA TTA TTT TGG GTG TGA AAT GC
2pDistal104-C
AGC CCA CTG CCA CAA TGT G
56 796
GGC AGC ATA GCC CCA GAC T
2pDistal104-D
TGA AGG ATC TTC CCC GGT AA
56 788
TCA GAG AAA GCA TTG CCT CCA T
2pDistal104-E
TGC GAA CAG CTG CTA GGT AGT TA
56 784
CCC CCA GAC AGG TGA ATG AG
2pDistal104-F
CAT CTT TCA AAC AAC CTG CAG TAG
56 769
GGG CAT TCA GGG AGC TCA T
2pDistal104-G
CCT CAG GCA GAG GCC AGT AC
56 766
AAT CAC AGG AGG CAA CCA CAA
2pDistal104-H
TCT GCC CCC CAA AGA CTT TT
56 757
GGC TGC CAT TTC AAA GCA AT
2pDistal104-i
CTC CTT CCC AGG GCA CTG TA
56 749
GTT TTG TTC CCA AAG GGA TCT G
2pDistal104-J
CCA GGA GCT CTC CCC AAG TAT
56 737
GTT GTT GAA GTT CTT TTT ATC CTC TTT G
68
2pDistal105
CAG CTA TAT TCT CCA GCC ATG GA
56 732
TGT CCT CCT TCC TTC CTG GTT
2pDistal106
TTC AGC CTC CAG CAT GGA A
56 676
TCT GTG TTC TGA CTC TGA CAG CAA
2pDistal107
CTG CTC TCC AAA GCC ACA CA
56 604
GGC GTT ATG GAT TTT GGA TGT T
2pDistal108
GTG TTA GAG TTA GCT CTG CCC TTG T
56 541
CCT TCA GTG GTG CCA TCT CA
2pDistal109
TTC CTG ACA AAT GTT GCC TAA TTC
56 465
TTG TGT GGA GGA AAC TTG GAG AT
2pDistal110
GAG GGA AAG TTC TGG TCA GGA A
56 407
GAA ACT AGA AGG GCC CAA ATG A
2pDistal111
CCA GCT TGC ACA TCC ACA AG
56 344
AGA ATG GCC TCC TCA GTT TGC
Markers used for fine refinement are italicized and were tested following larger scale refinement.
69
was compared to the control locus and the locus:control copy-number ratio was calculated. Cutoff values of 0.80 and 1.25 were used to indicate deletions and duplications, respectively.
All samples that appeared positive for a deletion or a duplication in the screening were retested in triplicate to confirm their status. As a positive control, each plate contained DNA from a
subject with a known rearrangement at the locus tested.
2.3 Association testing
2.3.1 SNP genotyping
Data from the HAPMAP project (http://www.hapmap.org) and Applied Biosystems were
used to determine haplotype structure in the genes tested. Tag SNPs, with a minor allele
frequency greater than 5%, were identified and selected using Haploview v3.32
(http://www.broad.mit.edu/mpg/haploview/) and Applied Biosystems’ SNPbrowserTM v3.5
(http://www.appliedbiosystems.com) in order to cover the major haplotype blocks in each gene.
SNP genotyping was carried out using validated custom TaqMan SNP assays (Applied
Biosystems). Genomic DNA (2 µL, 5 ng/µL) was added to the plate and dried on a heating plate
before the PCR reaction mix was added. Amplification was done in a 2.5 µL total reaction
volume on an ABI GeneAmp 9700 PCR machine, and the PCR products were scanned with an
ABI Prism 7900HT (http://www.appliedbiosystems.com) using standard conditions. Genotypes
were automatically generated with the SDS2.0 software using standard parameters and manually
verified to ensure call quality.
2.3.2 Statistical analysis
Prior to statistical analysis, each SNP was tested for deviation from Hardy-Weinberg
equilibrium in the subjects, their parents and the controls, using the HWE program (Ott, 1999).
Genotypes were checked for Mendelian errors with the FBAT program (v1.5.5) (Laird et al.,
2000). All families with Mendelian inconsistencies were either corrected following re-genotyping
70
or omitted from data analysis. LD between markers was determined using the 2LD program
(Zhao, 2004). Single-marker and haplotype family-based association testing (FBAT) was carried
out with FBAT v1.5.5. All family-based tests were performed under an additive model.
Allele and genotype frequencies in affected individuals were compared to those of the
comparison group using χ2 statistics with SPSS v12.0 (Chicago, IL). Haplotype frequency
predictions for controls and the affected individuals were calculated using the HAP program
(http://www.cs.columbia.edu/compbio/hap).
One affected individual per family was randomly chosen and used in the statistical analyses
except for family-based testing with FBAT, which can handle data from related triads, thus
allowing the inclusion of all individuals from MPX families.
2.4 Correction for multiple comparisons
False discovery rate (FDR), which controls the proportion of type I errors (incorrectly
rejected null hypotheses), was used to correct for multiple comparisons. To perform FDR
correction, with H1…Hn being the null hypotheses tested and P(1)…P(n) their corresponding Pvalues, ordered in increasing order, one has to find the largest m such that P( m ) ≤
m
α and reject
n
all H(m) for m = 1…m.
2.5 In silico analysis of breakpoints
The Human Genome Segmental Duplication Database (http://projects.tcag.ca/humandup/)
was used to examine the regions around the 2p deletions for the presence of segmental
duplications in the breakpoint regions and RepeatMasker (http://repeatmasker.org/) was used to
identify and locate interspersed repeats and low complexity DNA sequences. Breakpoint regions
were also compared to each other for similarities using the online version of William Pearson’s
LALIGN program (http://www.ch.embnet.org/software/LALIGN_form.html).
71
2.6 Sequencing
2.6.1 Primer design
Sequence data and intron/exon structure information on the GTF2i gene were obtained from
NCBI’s Entrez Genes database (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene). Primers for
amplification and sequencing reactions were designed using the online version of Primer3 (v.
0.4.0) (http://frodo.wi.mit.edu) (Rozen & Skaletsky, 2000) and were located at least 75 bp outside
of each region of interest since the first 50 bp of a sequencing reaction were usually unreadable.
Whenever possible, a single amplicon was used to sequence more than one exon to reduce cost.
All primers were aligned with the human genome with NCBI’s BLAST to ensure specificity.
Primer sequences are presented in Table 2.10.
2.6.2 DNA preparation and sequencing
Each exon and nearly 1 kb of promoter sequence were amplified using the PCR conditions
listed in Table 2.10. PCR reactions were carried out in 96-well plates with 5ng of DNA in a total
reaction volume of 25 µL. All PCRs were performed on a dyad MJ Research DNA Engine and 3
µL of each sample were separated on a 1% agarose gel to assess the amplification reactions.
Primers for sequencing were diluted to 1.6 µM and both primers and amplified materials
were sent to Université Laval’s Sequencing and Genotyping Services
(http://www.sequences.crchul.ulaval.ca) where PCR samples were purified on fiberglass
membranes in order to eliminate oligonucleotides, dNTPs and salts. PCR was carried out on the
samples using Big Dye terminators before the sequence was determined by capillary
electrophoresis on an ABI 3730xl Data Analyzer (Applied Biosystems).
72
2.6.3 Data analysis
Data files were analyzed with Applied Biosystems’ Sequence Scanner Software V1.0. For
each exon, sequence data was aligned with and compared to genomic data with the alignment
editor and sequence analysis program Bioedit V7.0.9.0
(http://www.mbio.ncsu.edu/BioEdit/bioedit.html).
2.6.4 Confirmation of mutations
The status of apparent mutations identified through sequencing was confirmed using
restriction-fragment length polymorphisms (RFLP). Primers were designed using the online
versions of Primer3 v0.4.0 and NEBcutter V2.0 (http://tools.neb.com/NEBcutter2/index.php)
(Vincze et al., 2003). Primer sequences and restriction enzymes used are listed in Table 2.11.
PCR reactions were carried out with 5 ng of DNA in a total reaction volume of 5 µL. All
PCRs were performed on a dyad MJ Research DNA Engine. An initial denaturation 95°C for 5
min was followed by 30 cycles of 95°C for 30 s, 55°C (except for exon 28: 60°C) for 30 s, and
72°C for 45 s. The final extension was at 72°C for 10 min. In all cases, primer concentrations
were 0.50 mM, and MgCl2 concentration was 1.5 mM. Restriction enzyme (5U) was added and
the reaction mixtures were incubated at the required temperature for 4h. The PCR fragments were
separated on a 1.5% agarose gel.
2.7 Informed consent
This study was approved by the Queen’s University ethics board and written informed
consent was obtained from all participating family members or through AGRE (Geschwind et al.,
2001).
73
Table 2.10 Primer sequences and amplification conditions for the sequencing of the 35 exons of GTF2i.
Exon
Amplification Primers
Name
Sequence
Pro2F
TCGGGGTTGTAAAGTTTTGG
to -475 bp Pro2R
ACCCCACATTCCTTGCAAAT
-499
CGCAATTTGCAAGGAA
-941
Pro1F
to -156 bp Pro1R
CCAGCGGGAGTTGTAGTTTC
1
E1F
GAAGCTGGGAGAGCAGAGAA
E1R
GATGAGGCGAGAGGGACTC
E2F
TAAAGTGCCTTGCCTCGTCT
E2R
GAGGAATGTCACCACCATCC
E3F
TCATCGAGCAGAAATGATGC
E3R
GGCTTAAAATTAATGAAATGCAAAAGA
E4F
CCAAATATTAAGGTAATGGATATGGA
E4R
ACACTTCCTCCTGGGTTCCT
E5+6F
TTCTGCTGACATGGGGAGAT
E5+6R
GCTCAATGAACCTTTTGTGGA
E5+6F
TTCTGCTGACATGGGGAGAT
E5+6R
GCTCAATGAACCTTTTGTGGA
E7F
GCCTAGTTCTACCCCACTAATTC
E7R
CCAAGTCAAAGAGGGCATGT
2
3
4
5
6
7
PCR conditions
Annealing
temp. (°C)
PCR [MgCl2]
buffer1 (mM)
Sequencing primers
Cycles Name
Sequence
55 or 60
2X
1,5
35
Pro2R
ACCCCACATTCCTTGCAAAT
60
2X
1,5
35
Pro1R
CCAGCGGGAGTTGTAGTTTC
60
1X
1,5
35
E1F
GAAGCTGGGAGAGCAGAGAA
60
1X
1,5
35
E2seqF
TGCCTTGCCTCGTCTTGTT
55
2X
1,5
35
E3F
TCATCGAGCAGAAATGATGC
55 or 60
2X
1,5
35
E4seqR CTGGGTTCCTTCAAAATGGA
60
1X
1,5
35
E5+6F
TTCTGCTGACATGGGGAGAT
60
1X
1,5
35
E5+6F
TTCTGCTGACATGGGGAGAT
60
1X
1,5
35
E7seqR CTGATATCTCAATTATCTAAC
74
8
9
10
11
12
13
E8F
CTTGGTCAAGGGAGGGATCT
60
1X
1,5
35
E8seqF
CCCAGAGACTTTGAATGCTG
E8R
TGCCTACCACTTACTGAGCAAA
E9F
TAGGGAGGGCAGAGAGGATT
55 or 60
2X
1,5
35
E9F
TAGGGAGGGCAGAGAGGATT
E9R
GAAATTAAAGCAATTGATGAGCA
E10F
AAGGCCATTCCATGTATCTCA
60
2X
1,5
35
E10F
AAGGCCATTCCATGTATCTCA
E10R
CAGTCTGTGCAATATAAGGAAACA
E11F
TCTCTTTCATCTTTCAATGTCAGTTT
55 or 60
2X
1,5
35
E11F
TCTCTTTCATCTTTCAATGTCAGTTT
E11R
GGAAGGCATACCAAGGGAAT
E12F
TGAATGAAAATGCCAGTGGA
55 or 60
2X
1,5
35
E12F
TGAATGAAAATGCCAGTGGA
E12R
ATTCTCACGGCCTCATTCTG
60
1X
1,5
35
E13R
CCTGGCTTTCCAAAGATGAG
60
1X
1,5
35
E14F
TGCTAAAAGACCCCTGGAGA
60
1X
1,5
35
E15+16F GGAGACACAGCAAGACTCCA
60
1X
1,5
35
E16seqF GCTCAAGCATCCAGATTATCTTT
E17+18F CAAGTTATTAAACCCATTAAATTGAGA 60
1X
1,5
35
E17+18F CAAGTTATTAAACCCATTAAATTGAGA
1X
1,5
35
E18seqF CCTGTGCCTTTCTTTGGATT
E13+14F ATGTTGGGAATTGACCAGGA
E13R
14
CCTGGCTTTCCAAAGATGAG
E13+14F ATGTTGGGAATTGACCAGGA
E13+14R ACATCACCCCCATGATGAAC
15
16
17
E15+16F GGAGACACAGCAAGACTCCA
E15R
AATGCCAACACTGCATGACT
E16F
GTTGTGCTCAAGCATCCAGA
E16R
TGAATTCTCCTGTAATGGCACA
E17+18R CAAAAGGCCCTTCTTCATCA
18
E18F
GACCTGTGCCTTTCTTTGGA
E18R
CAAAGTGCTGCGATTACAGG
60
75
19
20
21
22
E19F
GAGAAAGGCCTGTGAGTTGC
E19R
AGCCTGGCTGATCTGTGTTT
E20F
CTGATTTGCAACAGCACTGA
E20R
AGAGATGGGGTTTCATCGTG
E21F
GGCTCGTTCTTCAGATTTGC
E21R
ACGCCTGTCACCTCTAGCTT
E22+23F TGATCACATCACGACCGTTT
55 or 60
2X
1,5
35
E19F
GAGAAAGGCCTGTGAGTTGC
60
1X
1,5
35
E20seqF GATTTGCAACAGCACTGATA
60
1X
1,5
35
E21seqF TTCAGATTTGCCCCATATCC
60
1X
1,5
35
E22+23F TGATCACATCACGACCGTTT
60
1X
1,5
35
E23F
GAGCCCCTGTGGAGGATTAC
55 or 60
2X
1,5
35
E24F
AGGCTGGACTTGAAGATTGTAA
55 or 60
2X
1,5
35
E25+26R ACCCATCTCCCAGTGTCAAA
55 or 60
2X
1,5
35
E25+26R ACCCATCTCCCAGTGTCAAA
65-55TD; 602
1X
1,5
25+352 E27+28F AGCATGTGATTTCTGTTCTTCA
65-55TD; 60
1X
1,5
25+35 E27+28F AGCATGTGATTTCTGTTCTTCA
60
1X
1,5
E22+23R GCTAGTTTGGTTAACGTCATCTG
23
E23F
GAGCCCCTGTGGAGGATTAC
E22+23R GCTAGTTTGGTTAACGTCATCTG
24
25
E24F
AGGCTGGACTTGAAGATTGTAA
E24R
TCACCAATAGGAACAATTCACC
E25+26F CACTCCTGCCTGTGAATGTG
E25+26R ACCCATCTCCCAGTGTCAAA
26
E25+26F CACTCCTGCCTGTGAATGTG
E25+26R ACCCATCTCCCAGTGTCAAA
27
E27+28F AGCATGTGATTTCTGTTCTTCA
E27+28R GGAAGGCAACTGCTGTAGGA
28
E27+28F AGCATGTGATTTCTGTTCTTCA
E27+28R GGAAGGCAACTGCTGTAGGA
29
E29+30F CCAAGAGTTCAAGGCCAGTC
E29R
35
E29R
AAATTTAGCACAAACAACTCACTTTT
AAATTTAGCACAAACAACTCACTTTT
76
30
31
32
E30F
TGATCTTGTGCTTTTGACAGG
E30R
TGACACAAGTTATTTTGATTTGTTGA
E31+32F CGTAAATGACGTGGGCTAGA
E31R
CCCCAAAATCGGTGCTATTA
E32F
GGGCAACAAGAGCAAAACTC
60
1X
1,5
35
E30seqR GAAGCCGGATTCGATGACTA
60
1X
1,5
35
E31R
60
1X
1,5
35
E32ampF GGGCAACAAGAGCAAAACTC
60
1X
1,5
35
E33R
60
1X
1,5
35
E34seqF GGTGAATTGGATTTGCAGGT
60
1X
1,5
35
E35F
TGTGATATTCGTGACTGTTAAATTCC
E35R
GTCTGCGTCCCACCTACG
CCCCAAAATCGGTGCTATTA
E31+32R CACTGTGCAGGCAGGAGTAG
33
34
35
E33+34F TTCCTCCTCCTGGGTTCTTT
E33R
CTTAAGGACTTGCAGGACCAC
E34F
GGTGAATTGGATTTGCAGGT
E34R
AGGGCCACCATCTTATGTGA
E35F
TGTGATATTCGTGACTGTTAAATTCC
E35R
GTCTGCGTCCCACCTACG
CTTAAGGACTTGCAGGACCAC
1
The buffer provides the ionic strength and buffering capacity needed for the reaction and was used at a 1X concentration in standard conditions.
Ssalt concentration affects the melting temperature (Tm) of the primer-template duplex, and, therefore, the annealing temperature. In certain cases,
increasing the buffer concentration to 2X reduces or eliminates background noise caused by non-specific amplification.
2
Touchdown PCR – aimed at reducing non-specific amplification by gradually lowering the annealing temperature as PCR progresses. The
annealing temperature is higher in earlier cycles and is decreased in increments for every subsequent cycle. The first sequences amplified will be
the ones of greatest specificity, although with least efficiency than at lower temperatures. In subsequent cycles, the region of interest will outcompete nonspecific sequences . In this experiment, the annealing temperature was lowered from 65 to 55˚C over the first 25 cycles after which, a
regular 35-cycle PCR was carried out with a 60˚C annealing temperature.
77
Table 2.11 Primers and restriction enzymes used to confirm the status of mutations identified by
sequencing through RFLP experiments.
Exon Primers
15
GGGAGACACAGCAAGACTCC
Amplicon
size
Cut
size1
Enzyme
Cuts
(wt/m)2
237
211
AvaII / Sau96I
wt
220
188
RsaI
wt
226
172
RsaI / ScaI
m
250
148
Sai3AI / MboI
m
TCTAAAAGGAATTCCTTCAGGAG
18
CCAAAAATTTGAGGCACACC
CAAAAGGCCCTTCTTCATCA
28
29
TGATTTTAAATGTATATTTTCAGG
CACCAGGCAACGTGTAATGT
GCAACATCGTGAGAAACCAT
CCAACCATCATCTGCTGAAA
1
Size of PCR fragment after restriction-enzyme digestion.
2
“wt”- wild type: restriction enzyme cuts the wild-type allele; “m” – mutated: restriction
enzyme cuts only if mutation is present.
78
CHAPTER 3
Results
3.1 Del(X)(p22.1) in Subject 1
3.1.1 Array CGH data and sqPCR confirmation
Subjects with an ASD (115) were examined for the presence of genomic abnormalities
using array CGH in the laboratories of Drs E. Rajcan-Separovic and M.E.S. Lewis from the
University of British Columbia. Complex ASD cases are those that reached a DeVries score of 3
or more as described by de Vries et al. (2001), or cases that involve a combination of phenotypic
features including ID, minor and/or major craniofacial or growth anomalies medical problems
such as epilepsy, GI disturbances and/or systemic structural anomalies such as congenital heart
defect, renal defect and brain anomaly.
Array CGH showed that Subject 1 had a deletion corresponding to two clones on
chromosome Xp22.31, RP11-294K6 (6514 to 6537 kb) and RP11-143E20 (7634 to 7805 kb).
Real-time sqPCR using markers XpScreen1, 2 and 3 (Table 2.2) confirmed the deletion. Testing
of both parents indicated that the deletion was paternally inherited.
3.1.2 Breakpoints
A total of 16 and 17 sqPCR assays were used to refine the distal and proximal breakpoints,
respectively. The breakpoints were located between 6479 kb and 6542 kb (distal; Table 3.1) and
between 7870 kb and 7903 kb (proximal; Table 3.2) and the deletion therefore spanned between
1.33 and 1.42 Mb (Figure 3.1). It included 5 genes (Table 3.3).
79
6Mb
7Mb
RP11-366M24
(Balanced)
RP11-294K6
(Deleted)
8Mb
RP11-143E20
(Deleted)
RP11-657D19
(Balanced)
PN
4X
A4
PL
N
LG
S
S T 1A
D 7
H P1
HD 27A
S
RP
N
Deleted in Subject 1
Figure 3.1 Refinement of the breakpoints of the deletions on chromosome Xp22 in Subject 1
using real-time sqPCR. The BAC clones corresponding to the deletion in Subject 1 are indicated
with a black box and balanced clones with an empty box. The genes from RPS27AP17 to
PNPLA4 are deleted. Array-CGH experiments were performed by Dr Ying Qiao and Chansonette
Harvard.
80
Table 3.1 Distal Xp22 breakpoint refinement in Subject 1.
Probe
Position on chromosome (kb)
Deletion
XpDistal1
6 005
Bal
XpDistal2
6 067
bal
XpDistal3
6 129
bal
XpDistal4
6 207
bal
XpDistal5
6 266
bal
XpDistal6
6 344
bal
XpDistal7
6 403
bal
XpDistal8
6 479
bal
XpDistal9
6 542
del
XpDistal10
6 614
del
XpDistal11
6 675
del
XpDistal12
6 747
del
XpDistal13
6 813
del
XpDistal14
6 876
del
XpDistal15
6 983
del
XpDistal16
7 033
del
“del”: deletion (hemizygocity) detected; “bal” marker is balanced.
81
Table 3.2 Proximal Xp22 breakpoint refinement in Subject 1.
Probe
Position of chromosome (kb) Deletion
XpProximal1
7 606
del
XpProximal2
7 636
del
XpProximal3
7 730
del
XpProximal4
7 760
del
XpProximal5
7 870
del
XpProximal6
7 903
bal
XpProximal7
8 004
bal
XpProximal8
8 156
bal
XpProximal9
8 172
bal
XpProximal10
8 280
bal
XpProximal11
8 311
bal
XpProximal12
8 401
bal
XpProximal13
8 451
bal
XpProximal14
8 554
bal
XpProximal15
8 689
bal
XpProximal16
8 872
bal
XpProximal17
8 969
bal
“del”: deletion (hemizygocity) detected; “bal” marker is balanced.
82
Table 3.3 Genes deleted on chromosome Xp22.1 in Subject 1.
Gene symbol Gene name
Position on
chromosome (kb)
RPS27AP17
ribosomal protein S27a pseudogene 17
6917 - 6918
HDHD1A
haloacid dehalogenase-like hydrolase domain containing 1A 6977 - 7076
STS
steroid sulfatase (microsomal), isozyme S
7147 - 7283
VCX
variable charge, X-linked
7770 - 7772
PNPLA4
patatin-like phospholipase domain containing 4
7827 - 7855
83
3.1.3 Screening for additional cases of del(X)(p22.1)
Samples from 798 individuals with an ASD were screened for the presence of chromosome
abnormalities using sqPCR at three non-polymorphic markers located at 6.6 Mb, 7.1 Mb and 7.8
Mb (Table 2.2), all within the region deleted in Subject 1. No additional cases were found in the
autistic cohort and no deletions or duplications were found in the 186 controls tested.
3.1.4 Association of autism with the NLGN4X and VCX genes
Although no additional cases were identified, the deletion in Subject 1 could signal the
locations of ASD-related culprit genes. Two genes, NLGN4X and VCX were selected for further
association testing. Although NLGN4X lies approximately 350 kb distal to the deletion (Figure
3.1), it was included in the testing given its previously reported implication in ASD (see section
1.7.2.1). Five tag SNPs were genotyped in NLGN4X (rs1882411, rs5961933, rs10856356,
rs1997481 and rs1316392) and three in VCX (rs6530117, rs845125 and rs4118155; Table 3.4).
The SNPs tested in NLGN4X and VCX were in Hardy-Weinberg equilibrium in cases and
controls. There was an apparent increased transmission of the rs1316392-C allele for the
NLGN4X gene in the multiplex families (P = 0.005;) but not in the simplex families or the
combined group. This result, however, was not significant following FDR correction for multiple
testing. There was no evidence for preferential transmission of alleles for any of the three markers
tested in VCX.
As well, there was no evidence for an increased transmission of any NLGN4X haplotype
from the parents to affected individuals, either when pooling all families together, or when
including only simplex or multiplex families in the analyses (Table 3.5). The GAA haplotype for
the VCX markers rs6530117, rs845125, rs4118155 was apparently over-transmitted to affected
individuals in the combined families but not when testing only simplex or multiplex families (P =
0.04; Table 3.6) but the result was not significant following FDR correction.
84
Table 3.4 Family-based association testing of single markers within the NLGN4X and VCX genes with autism.
Gene
Marker
NLGN4X A (rs1882411)
B (rs5961933)
C (rs10856356)
D (rs1997481)
E (rs1316392)
VCX
F (rs6530117)
G (rs845125)
H (rs4118155)
Allele
T
C
T
C
T
G
T
C
C
T
G
C
T
A
G
A
Position on
1
chromosome (bp) fam#
5 982 382
301
6 045 657
250
6 058 854
303
6 062 796
299
6 063 605
224
7 766 313
264
7 786 598
306
7 788 006
272
All
Z
-0,118
0,118
0,121
-0,121
-1,22
1,22
1,08
-1,08
1,855
-1,855
1,302
-1,302
0,763
-0,763
-0,134
0,134
P
fam#
0,91
142
0,9
124
0,22
142
0,28
144
0,06
104
0,19
123
0,45
152
0,89
133
SPX2
Z
-0,012
0,012
1,163
-1,163
0,512
-0,512
-0,163
0,163
-0,583
0,583
0,725
-0,725
0,566
-0,566
-0,258
0,258
P
fam#
0,99
91
0,24
73
0,61
97
0,87
91
0,56
71
0,47
85
0,57
95
0,80
87
MPX3
Z
-0,298
0,298
0,243
-0,243
-1,007
1,007
1,536
-1,536
2,806
-2,806
0,792
-0,792
0,651
-0,651
0,676
-0,676
P
0,77
0,81
0,31
0,12
4
0,0050
0,43
0,51
0,50
1
fam#: number of informative families.
SPX: simplex families – families with only one affected individual.
3
MPX: multiplex families – two or more affected individuals.
2
4
Not significant after FDR correction: 0.005 >
1
× 0.05 ⇔ 0.0021
24
85
Table 3.5 Family-based haplotype association for the markers in the NLGN4X gene in families
with ASD.
Haplotype
Spx2
All
A B C D E fam#1
Mpx3
Z
P
fam#
Z
P
fam#
Z
P
T T G T C
187
0,42
0,67
82
-0,218
0,83
61
0,18
0,86
T T G C C
170
0,372
0,71
77
-0,469
0,64
49
1,121
0,26
C T T C T
125
-0,947 0,34
56
1,313
0,19
42
-1,399
0,16
C C T T C
143
0,544
0,59
68
-1,083
0,28
39
1,427
0,15
C T T C C
70
-0,379 0,70
31
0,733
0,46
28
-0,558
0,58
C C T C T
44
-0,516 0,61
26
-0,192
0,85
C C T T T
30
-0,608 0,54
15
0,258
0,80
C T T T C
24
0,686
0,49
13
-0,277
0,78
C C G T C
21
1,095
0,27
10
0,000
1,00
C T G T C
20
0,365
0,72
10
0,632
0,53
1
fam#: number of informative families.
2
SPX: simplex families – families with only one affected individual
3
MPX: multiplex families – two or more affected individuals.
86
Table 3.6 Family-based haplotype association for the markers in the VCX gene in families with
ASD.
Spx2
All
Haplotype
F
G
H fam#1
C
A
A
C
T
G
Mpx3
Z
P
fam#
Z
P
fam#
Z
P
272
-1,39
0,16
125
-0,55
0,58
83
-1,15
0,25
194
0,54
0,59
101
0,79
0,43
55
0,57
0,57
62
1,29
0,20
50
1,11
0,27
4
G
A
A
149
2,03 0,042
C
T
A
88
-0,71
0,48
45
-0,59
0,56
26
-0,82
0,41
G
T
G
82
-0,54
0,59
40
-1,23
0,22
31
0,38
0,70
G
T
A
88
1,09
0,28
39
1,27
0,21
23
0,15
0,88
1
fam#: number of informative families.
2
SPX: simplex families – families with only one affected individual
3
MPX: multiplex families – two or more affected individuals.
4
Not significant after FDR correction: 0.042 >
1
× 0.05 ⇔ 0.0028
18
87
3.2 Del(2)(p15-16.1) in Subjects 1 and 2
3.2.1 Array CGH data and sqPCR confirmation
In addition to the absence of 2 clones on chromosome Xp22.31, Subject 1 also had a deletion
representing the following DNA segments on 2p15-16.1: RP11-81L7 (57 007 to 57 160 kb),
RP11-90D1 (59 125 to 59 326 kb), RP11-81L13 (60 159 to 60 327 kb), RP11-79K21 (61 071 to
61 072 kb) and RP11-355B11 (61 513 to 61 673 kb).
The same DNA was also deleted in Subject 2 as well as DNA representing two telomeric
clones: RP11-494H5 (55 796 to 55 996 kb) and RP11-482H16 (56 343 to 56 542 kb). Both
deletions were confirmed with sqPCR with markers 2pScreen2, 3 and 4 (Table 2.2). Parental
testing revealed that the two deletions occurred de novo in Subjects 1 and 2.
3.2.2 Breakpoints
Real-time sqPCR was used to refine the breakpoints of the deletions. In Subject 1, they
were located between 63 173 kb and 63 199 kb (proximal; Table 3.7) and between 56 788 kb and
56 784 kb (distal; Table 3.8). In Subject 2, the proximal breakpoint was located between 63 364
kb and 63 393 kb (Table 3.7), while the distal breakpoint was between 55 564 kb and 55 534 kb
(Table 3.9). In Subject 1 the deletion was therefore between and 6.39 and 6.42 Mb, and in Subject
2, it was between 7.80 and 7.86 Mb (from 55.5 Mb to 63.4 Mb) ( Figure 3.2). The deleted region
common to both patients contained 40 genes or pseudogenes. It should be noted that the deletion
in Subject 2 spanned an additional 1.5 Mb distally and 9 genes or pseudogenes are known to be
located within the non-overlapping region.
88
Subject
Patient 11
Subject
Patient 22
Chabchoub et al.
De Leeuw et al.
Figure 3.2 Refinement of the breakpoints of the deletions in Subjects 1 and 2 using real-time
sqPCR. The BAC clones deleted in both Subjects 1 and 2 are indicated with a black box and
those balanced in both subjects are indicated with an empty box. Bi-colored clones were deleted
only in Subject 1. The genes from VRK2 to OTX1 are deleted in both patients. Array-CGH
experiments were performed by Dr Ying Qiao and Chansonette Harvard
89
Table 3.7 Proximal 2p16 breakpoint refinement in Subjects 1 and 2.
Position on
Deletion
chromosome (kb)
Subject 1 Subject 2
Probe
2pProximal1
61 520
del
del
2pProximal2
61 741
del
del
2pProximal3
61 968
del
del
2pProximal4
62 188
del
del
2pProximal5
62 415
del
del
2pProximal6
62 635
del
del
2pProximal7
62 878
del
del
2pProximal8
63 104
del
del
2pProximal8-A
63 119
del
nt
2pProximal8-B
63 142
del
nt
2pProximal8-B
63 173
del
nt
2pProximal8-D
63 199
bal
nt
2pProximal8-E
63 228
bal
nt
2pProximal8-F
63 255
bal
nt
2pProximal8-G
63 289
bal
nt
2pProximal8-H
63 308
bal
nt
63 322
bal
del
2pProximal9-A
63 338
nt
del
2pProximal9-B
63 364
nt
del
2pProximal9
90
2pProximal9-C
63 393
nt
bal
2pProximal9-D
63 429
nt
bal
2pProximal9-F
63 480
nt
bal
2pProximal9-G
63 511
nt
bal
2pProximal9-H
63 542
nt
bal
2pProximal10
63 556
bal
bal
2pProximal11
63 777
bal
bal
2pProximal12
63 881
bal
bal
Markers used for fine refinement are italicized and were tested following larger scale refinement.
“del”: deletion (hemizygocity) detected; “bal” marker is balanced; “nt”: not tested in this
individual.
91
Table 3.8 Distal 2p16 breakpoint refinement in Subject 1.
Probe
Position on chromosome
(kb)
Deletion
2pDistal101
56 992
del
2pDistal102
56 923
del
2pDistal103
56 866
del
2pDistal104
56 818
del
2pDistal104-A
56 813
del
2pDistal104-C
56 796
del
2pDistal104-D
56 788
del
2pDistal104-E
56 784
bal
2pDistal104-F
56 769
bal
2pDistal104-G
56 766
bal
2pDistal104-H
56 757
bal
2pDistal104-i
56 749
bal
2pDistal104-J
56 737
bal
2pDistal105
56 732
bal
2pDistal106
56 676
bal
2pDistal107
56 604
bal
2pDistal108
56 541
bal
2pDistal109
56 465
bal
2pDistal110
56 407
bal
2pDistal111
56 344
bal
Markers used for fine refinement are italicized and were tested following larger scale refinement.
“del”: deletion (hemizygocity) detected; “bal” marker is balanced.
92
Table 3.9 Distal 2p16 breakpoint refinement in Subject 2.
Position on chromosome (kb)
Deletion
2pDistal201
55 753
bal
2pDistal202
55 725
bal
2pDistal203
55 688
bal
2pDistal204
55 663
bal
2pDistal205
55 624
bal
2pDistal206
55 597
bal
2pDistal207
55 564
bal
2pDistal208
55 534
del
2pDistal209
55 505
del
2pDistal210
55 471
del
Probe
“del”: deletion (hemizygocity) detected; “bal” marker is balanced.
93
3.2.3 Screening for additional cases of del(2)(p15-16.1)
Samples from 798 individuals with an ASD were screened using sqPCR at 6 nonpolymorphic markers (56.1 Mb, 58.5 Mb, 59.2 Mb, 60.1 Mb, 60.9 Mb and 62.1 Mb; Table 2.2),
all within the region deleted in both subjects, for the presence similar chromosome abnormalities.
None were found. As well, no deletions or duplications were found in the 186 controls tested.
3.2.4 Association of autism with the OTX1 gene in the deleted region
Of the 40 genes/pseudogenes deleted in Subjects 1 and 2, OTX1was selected as the most
likely candidate gene for ASD. Three tag SNPs were selected for further association testing. All
three SNPs tested (rs6739804, rs2075375 and rs11125946) were in Hardy-Weinberg equilibrium
in both cases and controls. There was no evidence for an increased transmission of any allele
(Table 3.10) or haplotype (Table 3.11) from the parents to affected individuals, either when
pooling all families together, or when including only SPX or MPX families in the analyses.
Allele and genotype frequencies were calculated in subjects and controls and no significant
differences were found between the two groups (allele frequencies: χ2=0.035 – 1.160, df=1, P =
0.281 – 0.851; genotype frequencies: χ2= 0.006 – 0.444, df=2, P = 0.801 – 0.997). Similarly,
haplotype frequencies did not reach significance when comparing subjects and controls (P = 1.00;
Table 3.12). Given the previously reported involvement of OTX1 in epileptic seizures,
preliminary testing of a subgroup of individuals with epilepsy (n=32) was carried out but revealed
no association of ASD with specific alleles or haplotypes (data not shown).
3.2.5 Data bank analysis of breakpoint regions
Analysis using public databases revealed the absence of flanking LCRs sequences.
However, several DNA segments in the vicinity of the breakpoints were recognized by
Repeatmasker as being fragments of known interspersed or simple repeats, including SINEs and
94
Table 3.10 Family-based association testing of single markers within the OTX1 gene with autism.
Marker
A (rs6739804)
B (rs2075375)
C (rs11125946)
Allele
C
T
G
A
A
Position on
chromosome (bp)
fam#1
63 123 108
437
63 134 194
464
63 151 654
392
G
Spx2
All
Z
-0,138
0,138
1,067
-1,067
0,438
P
fam#
0,890
179
0,290
189
0,660
151
-0,438
Z
0,521
-0,521
1,125
-1,125
0,070
-0,070
Mpx3
P
fam#
0,600
121
0,260
130
0,940
113
Z
-0,109
0,109
0,000
0,000
0,000
P
0,910
1,000
1,000
0,000
1
fam#: number of informative families.
2
SPX: simplex families – families with only one affected individual.
3
MPX: multiplex families – two or more affected individuals.
95
Table 3.11 Haplotype family-based association testing of markers within the OTX1 gene with
autism.
Haplotype
Spx2
All
Mpx3
A
B
C
fam#1
C
A
G
416.5
-0,318 0,750 168.8
-1,040 0,300 109.0
0,546 0,590
T
G
A
350.7
0,387 0,700 129.6
-0,074 0,940 103.0
0,079 0,940
C
G
G
238.9
0,076 0,940 97.8
1,554 0,120 66.0
-1,149 0,250
T
G
G
108.0
0,987 0,320 52.1
0,179 0,860 24.0
1,397 0,160
T
A
A
20.5
-1,270 0,200 17.2
-1,318 0,190
T
A
G
16.8
-1,143 0,250 11.1
-0,490 0,620
C
G
A
23.6
Z
P
fam#
Z
P
fam#
Z
P
0,108 0,910
1
fam#: number of informative families.
2
SPX: simplex families – families with only one affected individual.
3
MPX: multiplex families – two or more affected individuals.
96
Table 3.12 Estimated frequencies of haplotypes in the OTX1 gene in affected individuals and
controls.
Haplotype
Cases1
CGA
23
0.017
5
0.014
CGG
190
0.139
48
0.129
CAG
686
0.506
189
0.507
TGA
357
0.264
103
0.278
TAG
66
0.048
17
0.047
TGG
16
0.012
6
0.016
2
17
0.013
3
0.010
Rare
Controls
P = 0.947
1
One affected individual was randomly selected in each multiplex family; all affected individuals
from simplex families were included.
2
All haplotypes with less than 5 occurrences in either cases or controls were pooled in the rare
haplotype category.
97
LINEs (short and long interspersed elements, respectively), long terminal repeats (LTRs) of
retrovirus-like sequences, and DNA transposon segments. Those stretches with greater than 60%
sequence identity and found at both the proximal and distal breakpoints were examined. Although
none were found for Subject 1, a 2960 bp segment with 87.9% identity was found at positions 63
386 kb and 55 575 kb in the region of the deletion in Subject 2. Repeatmasker identified these as
LINE-1 repeats
3.3 Dup(7)(q11.23) in Subject 3
3.3.1 Screening for cases with duplications or deletions at the WBS critical region
One individual (Subject 3) was found to have a duplication representing three clones,
B315H11, CTB-51J22 and B270D13 spanning the WBS region (Figure 3.3). The duplication was
confirmed with real-time sqPCR using two markers located within the involved region (73 113 kb
and 73 396 kb; Table 2.2).
3.3.2 Breakpoints and screening for additional cases
In Subject 3, a total of 23 and 26 non-polymorphic markers were quantified and compared
to control loci to identify the positions of the proximal and distal breakpoints, respectively (Table
3.13 and Table 3.14). The duplication spanned approximately 1.4 Mb and the breakpoints were
localized at ~72.4 Mb and ~73.8 Mb. This is located within the flanking low-copy repeats, blocks
Bc and Bm, that predispose to the genomic instability underlying the 7q11.23 deletion observed in
the majority of WBS cases (Figure 3.3).
To determine the frequency of dup(7)(q11.23), 798 individuals, with an ASD as well as
186 controls were screened for two markers within the duplicated region (Table 2.2) using realtime sqPCR. No additional cases of dup/del(7)(q11.23) were identified.
98
ABC
DE FG
STX1A
CYLN2
C c A c Bc Cm
RP11-35P20
H IJ
GTF2i
Bm Am
B315H11
CTB-51J22
B270D13
Bt At Ct
RP11-89A20
Common WBS deletion
Malenfant et al.
Edelmann et al.
Depienne et al.
ASD
Herguner et al.
Jacquemont et al.
Berg et al. (1 case)
ASD behaviours
Berg et al. (1 case)
Figure 3.3 Genomic overlap of the dup(7)(q11.23) with the typical WBS interval deletions and
with the rearrangements associated with ASD reported by others and genomic overlap.
Duplications are indicated in black and deletions in grey. The markers genotyped in each gene are
indicated. The three duplicated clones and flanking balanced clones are indicated with black and
white boxes, respectively. Array-CGH experiments were performed by Dr Ying Qiao and
Chansonette Harvard.
99
Table 3.13 Proximal 7q11 breakpoint refinement in Subject 3.
Probe
Position on chromosome (kb)
Duplication
7qProximal-A
71 674
bal
7qProximal-B
71 724
bal
7qProximal-C
71 809
bal
7qProximal-D
71 872
bal
7qProximal-E
71 935
bal
7qProximal-F
72 002
bal
7qProximal-G
72 048
bal
7qProximal-H
72 102
bal
7qProximal-I
72 187
bal
7qProximal-J
72 253
bal
7qProximal-K
72 332
bal
7qProximal-K1
72 335
bal
7qProximal-K2
72 344
bal
7qProximal-K3
72 350
bal
7qProximal-K4
72 365
dup
7qProximal-K5
72 374
dup
7qProximal-K6
72 381
dup
7qProximal-L
72 385
dup
7qProximal-M
72 431
dup
7qProximal-N
72 512
dup
7qProximal-O
72 575
dup
7qProximal-P
72 634
dup
7qProximal-Q
72 690
dup
Markers used for fine refinement are italicized and were tested following larger scale refinement.
“dup”: duplication detected; “bal” probe is balanced.
100
Table 3.14 Distal 7q11 breakpoint refinement in Subject 3..
Probe
Position on chromosome (kb)
Number of copies
7qDistal-A
73 355
dup
7qDistal-B
73 380
dup
7qDistal-C
73 409
dup
7qDistal-D
73 433
dup
7qDistal-E
73 465
dup
7qDistal-F
73 487
dup
7qDistal-G
73 515
dup
7qDistal-H
73 540
dup
7qDistal-I
73 586
dup
7qDistal-J
73 642
dup
7qDistal-K
73 702
dup
7qDistal-L
73 753
dup
7qDistal-L1
73 758
dup
7qDistal-L2
73 765
dup
7qDistal-L3
73 770
dup
7qDistal-L4
73 775
dup
7qDistal-L5
73 778
dup
7qDistal-L6
73 799
bal
7qDistal-L7
73 802
bal
7qDistal-L8
73 804
bal
7qDistal-M
73 806
bal
7qDistal-N
73 861
bal
7qDistal-O
73 921
bal
7qDistal-P
73 975
bal
7qDistal-Q
74 025
bal
7qDistal-R
74 081
bal
Markers used for fine refinement are italicized and were tested following larger scale refinement.
“dup”: duplication detected; “bal” probe is balanced; “nt”: not tested in this individual.
101
3.3.3 Family-based Association Tests
Three genes, STX1A, CYLN2 and GTF2i were tested for association in simplex and
multiplex ASD families, with three (rs1569061, rs4363087 and rs3793243), four (rs229890,
rs6964387, rs480487 and rs523434) and three (rs4717907, rs13227433 and rs2718270) tag SNPs,
respectively. All 10 SNPs tested in STX1A, CYLN2 and GTF2i were in Hardy-Weinberg
equilibrium in both family cohorts. Results from single-marker family-based association tests are
summarized in Table 3.15. There appeared to be an increased transmission of the rs1569061-T
allele in the STX1A gene in the combined families (P = 0.045), especially in multiplex families (P
= 0.037). However, these results were not significant following FDR correction for multiple
testing. There was no evidence for preferential transmission of alleles for any of the four markers
tested in CYLN2. There appeared to be increased transmission of the common alleles in combined
families (rs4717907-A: P = 0.039; rs13227433-G: P = 0.041) but again, the association was not
significant following FDR correction. It was not observed in the simplex families.
Increased transmission of the common alleles of two markers in GTF2i was found in the
multiplex families (rs4717907-A: P = 0.0010; rs13227433-G: P = 0.0032; Table 3.15) and these
results were significant following FDR correction. The association was not observed in the
simplex families. While FBAT results suggested that the association was also observed in the
combined cohort, (P = 0.039 and 0.041), this was not significant after FDR adjustment.
Haplotype TDT was undertaken for each gene (Table 3.16). There was no significant overtransmission of any haplotype in either STX1A or CYLN2 in the combined or simplex groups but
there appeared to be a modest over-transmission of the GTCG haplotype for CYLN2 in the
multiplex cohort (P = 0.040), which was not significant after FDR correction. The GTF2i AG
haplotype for the two SNPs (rs4717907 and rs13227433) that seemed to show preferential
transmission in the single marker studies, showed an over-transmission in the multiplex families
102
Table 3.15 Single-marker family-based association testing for the markers in the STX1A, CYLN2 and GTF2i genes in families with ASD.
Position on
All
Gene
Marker
Allele
1
chromosome (bp) fam#
Z
C
-2,00
172
STX1A A(rs1569061)
72 752 417
T
2,00
C
-1,04
440
B(rs4363087)
72 756 132
T
1,04
A
-1,55
447
C(rs3793243)
72 759 283
G
1,55
A
0,00
382
CYLN2 D(rs229890)
73 355 107
G
0,00
C
1,00
69
E(rs6964387)
73 365 179
T
-1,00
C
0,15
388
F(rs480487)
73 393 034
T
-0,15
A
0,34
371
G(rs523434)
73 398 640
G
-0,34
A
2,07
358
GTF2i H(rs4717907)
73 724 429
G
-2,07
G
2,04
364
I(rs13227433)
73 732 657
T
-2,04
A
0,29
192
J(rs2718270)
73 734 295
G
-0,29
1
fam#: number of informative families.
2
SPX: simplex families – families with only one affected individual.
3
MPX: multiplex families – two or more affected individuals.
4
Bold: P <0.05; Bold underlined: significant after FDR correction.
P
0,045
0,30
0,12
1,00
0,32
0,88
0,74
0,039
0,041
0,78
Spx2
fam#
Z
-0,58
96
0,58
-1,07
242
1,07
-1,49
249
1,49
0,12
215
-0,12
0,83
39
-0,83
-0,12
217
0,12
0,04
204
-0,04
-0,88
196
0,88
-0,49
196
0,49
-1,02
109
1,02
P
Mpx3
fam# Z
0,56
76
0,29
198
0,14
198
0,9
167
0,4
30
0,9
171
0,97
167
0,38
162
0,62
168
0,31
83
-2,09
2,09
-0,49
0,49
-0,79
0,79
-0,10
0,10
0,61
-0,61
0,30
-0,30
0,39
-0,39
3,30
-3,30
2,95
-2,95
1,16
-1,16
P4
0,037
0,62
0,43
0,92
0,54
0,77
0,69
0,0010
0,0032
0,25
103
Table 3.16 Family-based haplotype association for the markers in the STX1A, CYLN2 and GTF2i
genes in families with ASD.
STX1A
CYLN2
GTF2i
Group afreq1 fam#2
A B C
D E F G
S3
E(S)4 Var(S)5
Z
P6
H I J
T T G
G T C G
A G A
A G -
All
0,35 347,7 485,5
467,4
168,7 1,39
0,163
Spx
0,36 124,9 131,7
119,6
41,5 1,88
0,061
Mpx
0,36 178,8 340,0
323,0
111,9 1,60
0,109
All
0,70 319,0 691,9
685,1
153,6 0,55
0,582
Spx
0,70 115,0 181,0
175,0
43,0 0,92
0,359
Mpx
0,27 151,5 292,9
272,6
98,4 2,05
0,040
All
0,65 383,1 740,0
712,4
181,5 2,05
0,041
Spx
0,65 148,5 192,9
189,4
51,0 0,50
0,615
Mpx
0,65 170,0 480,0
444,9
108,3 3,38 0,0007
All
0,74 320,0 685,9
655,1
152,6 2,49
0,013
Spx
0,75 123,0 172,9
168,0
42,0 0,77
0,443
Mpx
0,74 146,0 460,9
427,3
92,9 3,49 0,0005
1
afreq: haplotype frequency in all families
fam#: number of informative families for the test.
3
S: number of occurrences of the haplotype in the affected offspring in informative families
4
E(S) expected S value under the null hypothesis of no biased transmission
5
Var(S) is the asymptotic variance.
6
Bold: P <0.05; Bold underlined: significant after FDR correction.
2
104
(P = 0.0007) as well as in the combined group (P = 0.041). After FDR correction, the association
was only significant in multiplex families. No over-transmission was observed in the simplex
families (P = 0.615). When SNP rs2718270, which was not shown to have an association in
single-marker analysis, was excluded, increased transmission of the AGA haplotype was found in
multiplex families and the combined cohort (P = 0.0005 and 0.013, respectively). Although the
association was strengthened in both groups, after FDR adjustment, it was still only significant in
the multiplex cohort.
3.3.4 GTF2i re-sequencing
Given the association of GTF2i with ASD in the multiplex families, the exons and 785 bp
of promoter sequence (from -941 to -156) of the GTF2i gene were re-sequenced in 7 individuals
with autism to identify any ASD-related mutations. Families that matched the following criteria
were selected (families matching all or most of the criteria listed in Table 3.17):
1) Multiplex family
2) Both parents are heterozygous for the AGA haplotype
3) All affected individuals are homozygous.
4) No unaffected child is homozygous for AGA.
Sequencing results pointed to five potential variations in the coding sequence of four of the
35 exons (Figure 3.4), four of which, if confirmed, would result in changes at the amino acid
level (Table 3.18). RFLPs were used to confirm the status of the 5 potential mutations and all 5
were found to be false-positives (Figure 3.5). Therefore, no sequence variants were found in the
exons of GTF2i gene.
105
Table 3.17 Families selected for GTF2i sequencing.
GTF2i AGA haplotype
Family
Multiplex
Both parents
All affecteds
No homozygous
heterozygous
homozygous
unaffecteds
22
Yes
Yes
Yes
N/A2
44
Yes
Yes
Yes
N/A
2250
Yes
Yes
Yes
Yes3
2253
Yes
Yes
Yes
Yes4
40084
No
Yes
Yes
N/A
40816
Yes
Yes
No1
N/A
41918
Yes
Yes
Yes
N/A
1
Two of three affected individuals were homozygous; the third individual is a
paternal half-sibbling.
2
N/A: all sibblings were probands.
3
One unaffected sister with no copy of the haplotype.
4
One unaffected brother heterozygous for the haplotype.
106
a)
80
85
90
A A K R A C T T C C T G A
b)
70
75
80
G A T C T G T RC G T G G AA
c)
35
30
25
G C A G T A S T AT T A A C
d)
70
65
60
55
T A A A G A G RT C A T T C AC T T G
Figure 3.4 Exon sequencing of GTF2i electrophoregrams showing apparent heterozygous
substitutions in subjects with ASD (a) shows an apparent heterozygous substitution on two
consecutive bases on exon 15 in proband 40084-2943 (b) apparent heterozygous substitution on
exon 18 in proband 41918-7688 (c) exon 28 in proband 22-78 (d) exon 29 in proband 44-134.
107
Table 3.18 Potential sequence variations within the GTF2i gene detected by sequencing.
Exon
Case ID
15
40084-2943
15
Position
Effect on amino
Expected
Observed
81
G
K (G/T)
Gly:STOP
40084-2943
82
G
R (A/G)
Gly:Glu
18
41918-7688
73
A
R (A/G)
Tyr:Cys
28
22-78
31
C
S (G/C)
Thr:Ser
29
44-134
65
C
Y (C/T)
no change
in exon (bp)
acid sequence
108
a)
b)
c)
d)
Figure 3.5 RFLP analysis for status confirmation of mutations detected by sequencing in GTF2i
exons. After amplification, the DNA was digested with a restriction enzyme using conditions preoptimized to ensure complete digestion (a) Proband 40084-2943 has wild type sequence at base
pairs 81 and 82 of exon 15 - AvaII (recognition site GGWCC) and Sau96I (GGNCC) digest the
amplicon when both base pairs are not mutated. Non-digested DNA (“nd”) served as the positive
control. (b) Proband 41918-7688 is wild type for base pair 73 of exon 18 – cleavage by RsaI
(GTAC) is only possible with the wild type sequence. Non-digested DNA (“nd”) served as the
positive control. (c) Wild-type sequence on exon 28 in proband 22-78 – RsaI (GTAC) and ScaI
(AGTACT) digest the amplicon in presence of the mutation. Non-digested DNA serves as
negative control. (d) Wild type exon 29 in proband 44-134 – Sau3AI (GATC) and MboI (GATC)
cleavage occurs on mutated sequence. Non-digested DNA serves as negative control.
109
CHAPTER 4
Discussion
4.1 Choice of experimental procedures and statistical analysis
Prior to discussing the results of this study, it is important to consider the various choices
of experimental procedures and statistical analysis methods that were available. A variety of
factors were considered prior to choosing a specific approach for data collection and analysis.
4.1.1 Real-time sqPCR – sensitivity and specificity
Array-CGH and FISH are widely used for the detection of DNA copy number variations.
FISH is time-consuming and atypical abnormalities may not be detected if the probe used does
not map precisely to the entire region. Array-CGH remains too expensive to screen large numbers
of patients for the moment. In contrast, real-time sqPCR is a well-established method for
detecting copy-number changes on the human genome (Weksberg et al., 2005). It is a rapid,
flexible and precise method which, compared with other methods for determining DNA copy
number, is more labor- and cost-efficient than most, including FISH and array-CGH.
Chromosome abnormalities can be screened in a high-throughput experiment with a set of lowcost, conventional PCR primers, using less than 10 ng of genomic DNA per sample. The SYBR
Green dye detection method used herein is more cost-effective than TaqMan™ assays, which
requires the synthesis of fluorescence-labeled probes for each unique sequence tested, although it
may sacrifice some accuracy. PCR primers can be designed to detect any region larger than 100
bp, making its resolution the highest of all currently available methods. The sensitivity of this
technique for the detection of genomic alterations is however limited in individuals with
mosaicism or balanced translocations. Apart from FISH, the same is true for other dosage
110
techniques such as array-CGH, Multiplex Amplifiable Probe Hybridisation (MAPH) and
Multiplex ligation-dependent probe amplification (MLPA).
As part of screening experiments, DNA from an individual with an abnormality at the locus
tested was included on each plate as a positive control. In each case (more than 20 experiments
per locus), the deletion/duplication was detected, suggesting that the rate of type II errors is
relatively low. Type I errors, on the other hand, were far more common, with 5 - 10% of cases
presenting with an apparent deletion or duplication. Those samples were routinely re-tested in
triplicate to confirm their status.
4.1.2 Family-based vs case-control association
Linkage analysis can be powerful for finding variants associated with a disease but the
regions identified are often very large, spanning as much as 40 cM. Association analysis provides
a higher resolution since several markers can be genotyped to test for LD between markers and
disease. A common strategy for the identification of complex disease-causing genes is to conduct
linkage analyses first and then follow up significant results with association testing (Cardon &
Bell, 2001). In this study, the initial screening for autism-susceptibility loci was carried out with
genomic microarrays and confirmed by sqPCR. Subsequent association analysis was carried out
using FBAT.
In terms of statistical power, TDT have similar power compared with case-control studies
when the number of triad families is equal to the number of cases and the number of cases is
equal to the number of controls for case-control studies (McGinnis et al., 2002). The use of
multiplex families can significantly increase the power of FBAT. Less than half the number
families is required to achieve the same power when families with two affected siblings are used
as compared with simplex families (Risch, 2000; McGinnis et al., 2002).
111
Collecting family data for TDT generally requires more resources in terms of time and
money (Laird & Lange, 2006), but family-based tests have the advantage of being valid even
when population stratification is present (Laird & Lange, 2006). Stratification is the inherent
presence of allele frequencies differences between populations due to non-random mating
between groups of individuals. The conventional approach to dealing with population
stratification is to match cases and controls according to a variety of variables. An important
amount of data therefore needs to be acquired, both for cases and controls, as the use of overly
broad categories for matching is not sufficient. For instance, although Caucasians of European
decent are often regarded as having a relatively homogeneous gene pool, there is still significant
genetic variation present among this group (Sokal et al., 1989). The need for additional
phenotypic data may offset the lower cost of recruiting single individuals (case-control) instead of
entire families (FBAT). For this study, samples from multiplex families were available for testing
and given the advantages of FBAT outlined above, this method was selected for statistical
analysis.
4.1.3 Correction for multiple comparisons
When multiple, independent, statistical tests are performed, the probability of Type I errors
(rejecting null hypotheses that should have been retained) increases and is not accurately reflected
by individual P-values. In genetic studies the risk of a false discovery is very high given the high
number of markers tested in each study. Colhoun et al. (2003) estimated that, in the case of
complex genetic disorders, up to 19 out of every 20 associations not corrected for multiple testing
reported in the literature could be false positives. It is therefore imperative to reduce false
discoveries while still detecting real associations.
Because family-wise error rate (FWER) methods, such as the Bonferroni correction, focus
exclusively on minimizing the risk of false discoveries, larger studies are penalized via the need
112
for very small P-values. This is not a major difficulty for the identification of Mendelian genetic
diseases. However, in the case of complex genetic disorders, where there are several causative
genes, each with a modest contribution to the phenotype, the power to detect association at any
given locus is limited. Thus, sacrificing power is more detrimental to the overall goal of finding
culprit genes than allowing occasional false positives (Sabatti et al. 2003).
Rather than controlling the probability that a given study will produce one or several type 1
errors, it may be more beneficial to use a correction method that would ensure the same ratio of
false positives, regardless of study design. FDR, proposed by Benjamini and Hochberg (1995), is
defined as the expected proportion of null hypotheses rejected by mistake among all rejected
hypotheses. In other words, FDR determines the proportion of false positives among all
significant tests. It is a method for multiple comparisons correction that aims at balancing the
need to reduce the probability of false positives while still detecting true positives, which makes it
an ideal correction method for complex genetic disorders such as ASD.
In genetics, correlated tests are expected because of linkage disequilibrium between
markers but the exact correlation factor cannot be determined with exactitude. In the present
study, multiplex and simplex families were tested independently and also as part of a combined
group. This third group is therefore not totally independent from the other two. Thus, by
considering each marker tested in each group as a separate, independent, test, for FDR purposes,
the correction applied can be considered as being relatively prudent and conservative. The
positive associations findings reported herein are therefore relatively unlikely to be the result of
type I errors.
113
4.2 The genetics of ASDs
Family and twin studies have shown that ASDs are complex genetic disorders. Results
from genome screens and candidate gene studies (reviewed by Gupta & State MW, 2007) suggest
that more than 20 genes may be involved in the aetiology of these disorders (Risch et al., 1999).
Despite an estimated heritability of >90%, the exact causes of ASDs remain elusive as gene
discovery efforts have, so far, pointed to multiple distinct loci in small subsets of individuals.
Because their underlying cause remains unknown, there are no laboratory tests available
to diagnose ASDs; current diagnostic methods rely on the observation of social behaviour and
cognitive abilities, which can be considered arbitrary (Lord et al., 1994; Lord et al., 2000). These
symptoms have heterogeneous presentation in different patients and may also vary over time
within certain individuals. Because the core symptoms of autism are not measured with discrete
(qualitative) variables, but rather with continuous, quantitative scales, patients with a shared
diagnosis (eg: PDD-NOS or autistic disorder) may present with highly variable clinical
symptoms. Also, other phenotypes such as apraxia, epileptic seizures, sensory abnormalities,
gastrointestinal symptoms and sleep disturbance, which commonly accompany the core ASD
symptoms, are not required for an ASD diagnosis. Finally, some clinical symptoms such as
mental retardation are not specific to ASD and are observed in other psychiatric disorders. Thus,
there is a degree of phenotypic heterogeneity even amongst individuals with shared diagnoses.
Phenotypic heterogeneity suggests that allelic or locus heterogeneity likely plays a role in
the aetiology of ASDs. Locus heterogeneity indicates that variations in different genes may cause
the similar and/or overlapping clinical features whereas allelic heterogeneity refers to different
variations at a single locus leading to identical or overlapping phenotypes. In addition to allelic
and locus heterogeneity, other non-Mendelian inheritance factors such as incomplete penetrance,
gene-gene and gene-environment interactions may also hinder the search for ASD genes.
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In an attempt to circumvent genetic heterogeneity, many groups have turned to
subgrouping individuals into phenotypically homogeneous clusters. For instance, Buxbaum et al.
(2001) reported a LOD score of 2.4 at 2q31.3 in a group of 95 ASD families. The LOD score was
increased to 3.3 in a subset of 49 families meeting a strict diagnosis of autism with the presence
of speech delay. Shao et al. (2002) also performed linkage analysis of chromosome 2q and
obtained a LOD score of 1.1 at 2q33 in 99 families, which was improved to 2.9 in a subgroup of
45 families with speech delay.
These studies show that subgrouping individuals according to endophenotypes represents a
potential fruitful avenue for the identification of ASD-causing genes. It is important to remember,
however, that data from twin studies have shown that individuals with identical genomes are not
always affected equally and in some cases, they can even fall in different diagnostic categories
(Bailey et al., 1995). For subgrouping to be effective, the endophenotypes used need to bear a
closer relationship to the biological processes that are involved in causing ASD than the diagnosis
itself. In other words, because the effect of a genetic locus contributing to a specific
endophenotype is expected to be larger than the effect of those contributing to a broadly defined
ASD diagnosis, the probability that linkage or association will be detected is expected to be
greater. This will obviously not be the case for all potential endophenotypes. In fact, it has been
argued that the genetic architecture of endophenotypes may not be much simpler than that of
psychiatric diagnostic categories (Flint & Munafo, 2007).
Given the great challenge posed by the heterogeneity of ASDs, alternatives to traditional
gene mapping approaches need to be explored. New array-based techniques allow the genomewide detection of chromosome abnormalities at a resolution that is far greater than traditional
cytogenetic methods. Detection of CNVs is not dependant on the homogeneity of the population
tested but rather on the large number of individuals screened.
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4.3 Copy-number variations and autism spectrum disorders
With the advent of genome-wide screening methods for copy number changes, a significant
amount of new cytogenetic abnormalities, involving all chromosomes, have been reported in
association with ASD and several other disorders. The majority, however, have been found in
single cases and their importance remains unknown. Chromosome abnormalities are more
prevalent in complex (or syndromic; 27.5%) ASD cases, presenting with ID and/or dysmorphic
features, than in simple cases (7.2%) (Jacquemont et al., 2006; Sebat et al., 2007).
The main purpose of this study was to characterize novel genomic abnormalities, expected to
signal the location of ASD-related genes, and to identify these autism-susceptibility genes. The
overall hypothesis was that deletions and duplications found in individuals with ASD would
signal the locations of ASD-related genes. I was guided by my four objectives of localizing and
characterizing novel deletions and duplications in individuals with ASD, screening for additional
cases in a large group of unaffected controls and individuals with ASD, identifying candidate
genes and testing them for association with ASD and, examining the presence of mutations in the
coding region of candidate genes found to be associated with ASD.
Up to 20% of the human genome is now believed to consist of CNVs (Lee et al., 2007),
most of which are benign polymorphisms, observed in healthy individuals. There is therefore a
need to differentiate between benign CNVs and those that are likely to be pathogenic. Publicly
available databases such as the Database of Genomic Variants (DGV http://projects.tcag.ca/variation/) and the Database of Chromosomal Imbalance and Phenotype in
Humans using Ensembl Resources (DECIPHER - http://decipher.sanger.ac.uk/) list human
CNVs, that are either benign or associated with a specific phenotype.
Definitive criteria for selection of CNVs as potentially pathogenic have not yet been
established, but CNVs absent from controls and occurring do novo in patients or inherited from
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an affected parent are generally of more interest. Genomic abnormalities associated with a
specific clinical syndrome, or involving genes for which a dosage effect has been reported are
also commonly selected for further investigation (Fan et al., 2007). Similarly, CNVs overlapping
regions of known CNV-associated disorders are also potentially pathogenic. The nature and size
of a CNV must also be considered; duplications are generally better tolerated than deletions
(Brewer et al., 1999). Most benign deletions found in healthy subjects average 15-20 kb or
smaller (Conrad et al., 2006). Although genomic abnormalities as large as 10 Mb in size have
been observed in healthy individuals (Hansson et al., 2007), larger CNVs are more likely to
encompass several genes and are generally associated with a significantly defined phenotype.
While the occurrence of a specific CNV in healthy controls is generally indicative of a lack
of pathogenicity, issues such as mosaicism, incomplete penetrance, variable expression,
positional effect, and gene-gene interactions could modify biological functions and need to be
considered. A single-copy deletion detected in healthy controls may be associated with
pathogenicity if it uncovers a recessive mutation on the other, unaltered, copy of the
chromosome. Finally, more stringent standardizations and validations of CNV data from public
databases is required as much of the information they contain is obtained from studies using a
single platform (Scherer et al., 2007). Data obtained as part of this study showed that as much as
one in four genomic abnormalities initially detected by array-CGH are ultimately not confirmed
by sqPCR. This rate is similar to those reported by others (Bar-Shira et al., 2006).
4.4 Genomic rearrangements characterization and candidate genes testing
4.4.1 Del(X)(p22.31)
Characterization of a 1.4 Mb (6.5-7.9 Mb) deletion on the long arm of chromosome X from
a nonverbal female with autistic disorder as well as dysmorphic features, moderate to severe ID,
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and ADHD showed breakpoints between 6479 kb and 6542 kb (distal; Table 3.1) and between
7870 kb and 7903 kb (proximal; Table 3.2). No additional cases of either deletion or duplication
at Xp22.31 were found in 798 individuals with ASD and 186 controls, and no similar cases have
been reported from 5 large-scale screening studies (Christian et al., 1999; Jacquemont et al.,
2006; Sebat et al., 2007; Szatmari et al., 2007; Marshall et al., 2008). These studies included at
least 3410 subjects with an ASD evaluated for subchromosomal CNVs.
A high frequency of CNVs has been reported for the distal region of the short arm of the X
chromosome (Ballabio et al., 1989; Ballabio & Andria, 1992; Fukami et al., 2000; Van Esch et
al., 2005; Chocholska et al., 2006; Zinn et al., 2007; Shinawi et al., 2009). The deletions,
duplications and translocations identified have been associated with various phenotypes including
short stature, X-linked ichtyosis, ID, Kallmann syndrome, ocular albinism type 1 (OA1) and, in
some cases, autism. As a result, several disease genes have been mapped to Xp22, including Xlinked chondrodysplasia punctata (arylsulfatase E ; ARSE), short stature (short stature homeobox;
SHOX), X-linked ichthyosis (Steroid sulfatase; STS), Kallmann syndrome (KAL), and ocular
albinism type 1 (G protein-coupled receptor 143; GPR143) (Ballabio et al., 1989; Weissortel et
al., 1998; Wandstrat et al., 2000; Melichar et al., 2007). Although this region has a high CNV
frequency, there are no obvious recombination hotspots for deletions on Xp22. Breakpoints of
reported genomic abnormalities are distributed throughout the region (Zinn et al., 2007). No
LCRs or other repetitive elements within or in the vicinity of the breakpoints in Subject 1 were
found, which likely explains the low occurrence of the deletion.
With a known male-to-female ration of 4:1, being male remains the strongest risk factor for
ASD to date. A higher ratio of 6.5:1 is found in patients with essential autism or autism without
significant dysmorphologies (Miles et al., 2005). Moreover, females with essential autism
generally present with less severe symptoms than males (Miles et al., 2005). This suggests an
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involvement of sex chromosomes in at least a subset of autism cases. Reports of chromosome
abnormalities on the sex chromosomes of individuals with autism (Gillberg, 1998; Wassink et al.,
2001; Geerts et al., 2003) and the presence of autistic features in individuals with Fragile X and
Rett syndromes, two X-linked disorders, support this hypothesis.
As is well known, females have two functional copies of each gene on the X-chromosome,
while males may have one or two, depending on whether there is a Y-linked homologue. During
embryonic development, one of the two X chromosomes of females is randomly and permanently
inactivated in each somatic cell. Microdeletions including genes subject to X inactivation in
females are thus expected to cause a functional nullisomy for these deleted genes in a proportion
of cells. Although the Xp22.31 deletion in Subject 1 was inherited from a neurotypical father,
VCX and NLGN4X both have Y-homologues, with which they share a high degree of sequence
identity, and which are transcriptionally active. Thus, I hypothesize that in the father, these Yhomologues are sufficient to compensate for the loss of their X-linked counterpart but, in the
daughter, no such compensation is possible in the cells where X-inactivation of non Xp22 deleted
chromosome occurred. Indeed, deletions on the X-chromosome are commonly paternal in origin.
For example, there is a loss of the paternal X chromsome in 20/29 Turner syndrome patients
(Jacobs et al., 1990). James et al. (1998) reported that the abnormal X chromosome was paternal
in origin in 19 of 21 females with de novo deletions on chromosome Xp. Finally, in a clinical
study of females with deletions of the short arm of chromosome X by Lachlan et al. (2006), all 19
de novo abnormalities were of paternal origin.
Although no additional cases of Xp22.31 deletions were found in the ASD cohort, this
seemingly rare abnormality may signal the location of an autism gene or genes. Two loci in the
region, NLGN4X and VCX were selected for association testing. Although NLGN4X lies
approximately 350 kb distal to the deletion (Figure 3.1), it was included in the association testing
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given previous reports of its implication in ASD (Jamain et al., 2003; Laumonnier et al., 2004)
and because position effects can not be excluded. There was an apparent increased transmission
of the rs1316392-C allele in the NLGN4X gene in multiplex families but this was not significant
following FDR correction. Similarly, transmission to affected individuals of the GAA haplotype
for the VCX markers rs6530117, rs845125 and rs4118155, was apparently increased in the
combined group (P = 0.04; Table 3.6) but this was not significant following FDR correction.
Thus, in the two ASD cohorts (simplex and multiplex) tested in this study, there was no evidence
for association with ASD of any of the tag-SNPs tested in NLGN4X or VCX.
Although tag-SNPs are expected to be representative of the allelic variation on a genomic
region, it is possible that rare genetic variations were not detected. Moreover, haplotypes cannot be
obtained directly but rather have to be inferred from genotyping data. The accuracy of the process
depends of the availability of extended pedigrees and on the absence of recombination between
SNPs. While there is generally very little recombination between markers over a small genomic
region, the recruitment of large multi-generation pedigrees is a difficult and costly task.
Given that deletions of the Xp22 region are occasionally associated with an autistic
phenotype in females (Thomas et al., 1999; Chocholska et al., 2006), the pathogenicity of the
Xp22.31 deletion observed in Subject 1 cannot entirely be excluded. It is likely, however, given
the confirmation of paternal inheritance of the Xp22.31 deletion from a neurotypical father and
the negative association results, that the phenotypic traits shared with her brother (Subject 2) are
associated with the shared microdeletion on chromosome 2. This is supported by the report by
Mochel et al. (2008) of a deletion including NLGN4X and VCX in an individual with normal IQ,
behaviour, social skills, and psychomotor development. Nevertheless, these loci have been
studied in association with ASD previously and a more detailed examination is warranted. The
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following two subsections examine these two loci, which still have potential for association with
autism, in more detail.
4.4.1.1 VCX
There has been no report of association studies on any of the VCX/Y genes in relation to ASDs.
However, other studies on Xp22 chromosome abnormalities have provided evidence that
deletions of VCX-3A are associated with mental retardation, suggesting that other members of the
VCX/Y family may be associated with intellectual impairment (Ballabio et al., 1989; Muroya et
al., 1996; Fukami et al., 2000). However, when Fukami et al. (2000) re-sequenced VCX, VCX-2
and VCX-3A in five unrelated patients with MR, whose families showed linkage to Xp22, there
was no mutations in any of the three genes. Taken together, the involvement of each VCX gene in
the etiology of intellectual impairment is difficult to interpret. A group of related individuals with
an Xp22.3 deletion encompassing SHOX, ARSE, NLGN4X, but not VCX-3A presented with severe
learning disabilities and ADHD (Boycott et al., 2003). Lesca et al. (2005) studied 7 related males
affected with X-linked ichthyosis caused by a microdeletion of the STS gene on chromosome
Xp22.3, which also included the VCX and VCX-3A genes. Six of these patients presented with
normal mental development while one had moderate psychomotor delay.
The function of VCX/Y family members is unknown but it has been suggested, due to their
small size and high charge when conceptionally translated that they may encode chromatinassociated proteins (Lahn & Page, 2000). The N-terminal domain, which is conserved amongst all
VCX/Y members, contains several basic residues and is therefore positively charged. In contrast,
their C-terminal domain contains a number of acidic amino acids and carries a negative charge. It
is made up of 30 bp repeat motifs (or repeat unit 1; RU1) that vary from 1-14 copies in different
VCX/Y genes. Thus, VCX, VCX3A and VCX3B, which contain 10, 8 and 11 RU1 repeats,
respectively, will be more negatively charged than VCX2, VCY and VCY1B, which only contain 2,
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1 and 1 repeats, respectively. Van Esch et al. (2005) suggested a dosage-dependant mechanism in
which the combined action of VCX/Y family members determines their overall effect rather than
each individual protein being responsible for specific clinical symptoms. Under this model, if
chromatin-associated VCX/Y proteins are involved in the etiology of ASD, it could be through
alterations of the expression of other loci.
4.4.1.2 NLGN4X
As indicated, the Xp22 deletion in Subject 1 did not include NLGN4X, but it maps close to
one of the breakpoints. A frameshift (nonsense) mutation was found in the NLGN4X gene in two
Swedish families (Jamain et al., 2003), and since that time, replication studies have aimed at
confirming the role of NLGNs in ASD. Additional (non-synonymous) mutations have been
reported in individuals with autism, mental retardation or PDD-NOS (Blasi et al., 2006;
Laumonnier et al., 2004; Yan et al., 2005). However, mutation screening in a large group of ASD
probands failed to identify coding variants in NLGN genes (Talebizadeh et al., 2004; Vincent et
al., 2004; Gauthier et al., 2005; Ylisaukko-Oja et al., 2005; Wermter et al., 2008). Ylisaukko-Oja
(2005) performed association testing of four microsatellite markers in the NLGN4X gene and
reported a modest association of marker DXS996 with ASD (P = 0.031). These results were not
corrected for multiple testing and the authors acknowledged that the association would not remain
significant if any type of correction were to be applied. There has been, to date, including this
thesis, no convincing report of a positive association of NLGN4X polymorphisms with ASD.
The effect of rare alleles, even those with a strong phenotypic effect, is difficult to detect
by association studies, since tag SNPs are designed to capture common polymorphisms, generally
with minor allele frequencies greater than 5%. A number of rare variants have been identified in
the NLGN genes, including NLGN4X, each in a small number of ASD probands. Additional rare
variants in genes of the neuroligin-neurexin pathway have also been identified in ASD families
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(see section 1.7.2.1). The contribution of mutations in NLGNs remains unclear but appears to
follow the rare variant – common disease model. To obtain positive association results of culprit
genes under this model, genetic heterogeneity would have to be reduced through effective
subgrouping, which cannot yet be achieved, or, by studying non-randomly mating populations.
4.4.2 Del(2)(p15-p16.1)
Characterization of two overlapping 2p15-p16.1 deletions in two unrelated individuals,
Subjects 1 and 2, with autism or autistic features, appeared to hold promise for identifying
important genes associated with this disorder. Breakpoint refinement allowed determination of
the DNA that was deleted in both individuals. The common 6.3 Mb deleted region included 40
genes and pseudogenes and the additional 1.5 Mb deleted in Subject 2 included 9 genes and
pseudogenes (Table 4.1). In silico analysis using public databases revealed no flanking LCRs, but
there were 108 other repetitive elements, such as SINEs and LINEs, in the regions of the
breakpoints raising the possibility that the two deletions may have arisen through NAHR (Korbel
et al., 2007). However, this is unlikely for Subject 1.
When I compared the repeats at the ends of each deletion, no region of sequence
homology equal to or greater than 60% for any stretch of sequence longer than 200 bp was found
in Subject 1. For Subject 2, there was a 2960 bp region with 87.9% homology at the proximal and
distal deletion breakpoints. Repeatmasker identified this region as belonging to LINE-1 repeats.
LINE-1 retrotransposons comprise approximately 15% of the human genome, with more than
100,000 copies but more than 95% are 5’ truncated (Smit, 1996). I confirmed such truncation for
the proximal breakpoint but not for the distal breakpoint. Because of their large copy number,
LINE-1 retrotransposons have been implicated in genomic rearrangements. Korbel et al. (2007)
reported that LINE-1 elements were significantly enriched at the breakpoints of copy-number
variants when compared to the genomic background. LINE-1 elements present in both
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Table 4.1 Genes uncovered by the deletions in Subjects 1 and 2 and their position on
chromosome 2.
Gene symbol Gene name
coiled-coil domain containing 104
SMEK homolog 2, suppressor of mek1
(Dictyostelium)
PNPT1
polyribonucleotide nucleotidyltransferase 1
EFEMP1
EGF-containing fibulin-like extracellular matrix
protein 1
MIRN217
microRNA 217
MIRN216A
microRNA 216a
MIRN216B
microRNA 216b
LOC100129434 hypothetical protein LOC100129434
CCDC85A
coiled-coil domain containing 85A
similar to Eukaryotic translation initiation factor 2
LOC647016
subunit 2 (Eukaryotic translation initiation factor 2
beta subunit) (eIF-2-beta)
CCDC104
SMEK2
LOC100131953 hypothetical LOC100131953
VRK2
FANCL
LOC339799
LOC644456
LOC730134
LOC647038
BCL11A
LOC442017
LOC130865
ATP1B3P1
PAPOLG
REL
LOC344423
PUS10
PEX13
KIAA1841
LOC339804
AHSA2
USP34
vaccinia related kinase 2
Fanconi anemia, complementation group L
hCG15200
similar to RED protein
hypothetical protein LOC730134
hypothetical LOC647038
B-cell CLL/lymphoma 11A (zinc finger protein)
interferon induced transmembrane protein
pseudogene
similar to 60S ribosomal protein L26 (Silicainduced gene 20 protein) (SIG-20)
ATPase, Na+/K+ transporting, beta 3 pseudogene
poly(A) polymerase gamma
v-rel reticuloendotheliosis viral oncogene homolog
(avian)
similar to ribosomal protein S12
pseudouridylate synthase 10
peroxisome biogenesis factor 13
KIAA1841
hypothetical gene supported by AK075484;
BC014578
AHA1, activator of heat shock 90kDa protein
ATPase homolog 2 (yeast)
ubiquitin specific peptidase 34
Position on chromosome (kb)
Start
End
55 600
55 629
55 626
55 698
55 716
55 947
55 774
56 004
56 064
56 070
56 081
56 254
56 265
57 129
56 064
56 070
56 081
56 266
56 467
57 134
57 846
57 847
58 127
58 240
58 332
58 541
59 319
59 376
60 532
60 763
58 241
58 322
58 338
58 543
59 331
59 469
60 634
60 764
60 792
60 793
60 815
60 837
60 962
60 817
60 880
61 004
61 018
61 023
61 098
61 147
61 226
61 020
61 099
61 130
61 219
61 245
61 258
61 268
61 268
61 551
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LOC100130280 hypothetical LOC100130280
61 330
61 331
small nucleolar RNA, H/ACA box 70B
SNORA70B
exportin 1 (CRM1 homolog, yeast)
XPO1
LOC100132037 similar to mCG7602
61 498
61 559
61 670
61 498
61 619
61 670
similar to 60S ribosomal protein L14 (CAG-ISL 7) 61 791
LOC647077
hypothetical protein FLJ13305
61 907
FLJ13305
61 939
LOC100127901 hypothetical LOC100127901
61 792
61 935
61 947
61 949
61 986
62 222
61 969
62 217
62 228
LOC100131923 hypothetical protein LOC100131923
62 238
62 292
UDP-GlcNAc:betaGal beta-1,3-Nacetylglucosaminyltransferase 2
LOC100130512 hypothetical LOC100130512
62 277
62 305
62 430
62 446
transmembrane protein 17
TMEM17
LOC100129141 similar to ribosomal protein L21
62 581
62 613
62 587
62 614
LOC100129162 similar to hCG1644578
62 634
62 642
EH domain binding protein 1
EHBP1
LOC100132215 hypothetical protein LOC100132215
62 787
63 126
63 127
63 129
63 131
63 202
63 138
63 669
CCT4
COMMD1
LOC729209
chaperonin containing TCP1, subunit 4 (delta)
copper metabolism (Murr1) domain containing 1
similar to 40S ribosomal protein SA (p40) (34/67
kDa laminin receptor) (Colon carcinoma lamininbinding protein) (NEM/1CHD4) (Multidrug
resistance-associated protein MGr1-Ag)
B3GNT2
OTX1
LOC51057
orthodenticle homeobox 1
hypothetical protein LOC51057
The genes deleted in both patients are indicated in bold. Others were only deleted in Subject 2.
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breakpoints of the deletion found in Subject 2 could predispose to genomic instability through
NAHR. Such a mode of deletion generation raises the possibility of recurrence of deletions in this
region.
Smaller, overlapping rearrangements involving the described 2p15-16.1 region have been
reported in control subjects, including a recurrent 2.9 Mb deletion/duplication in healthy
individuals (58.2-61.1 Mb; http://projects.tcag.ca/variation/) encompassing many genes such as
PEX13 , v-rel reticuloendotheliosis viral oncogene homolog (avian) (REL), and FANCL. A
subject with a de novo duplication of band 2p16.1 (clones RP11-201J10 to RP11-44OP5; ~57.1
Mb-60.7 Mb) was reported in the Decipher database
(https://enigma.sanger.ac.uk/perl/PostGenomics/decipher/; patient CHG00001375). The subject
did not resemble the two subjects studied here (Rajcan-Separovic et al., 2007) and did not have
an ASD. In fact, recurring de novo microdeletions of various sizes have been detected within the
2p15-16.1 region (Chabchoub et al., 2008; de Leeuw et al., 2008), that are not associatiated with
confirmed ASD. The latter subjects did have several phenotypic traits overlapping with those
found in Subjects 1 and 2, including ID and facial dysmorphologies such as high palate, smooth
upper vermillion border, everted lower lip, broad and high nasal root, and telecanthus. It was
proposed by Chabchoub et al. (2008) that the common features may be associated with five genes
present within the 570 kb deletion. This smallest critical region is common to the two patients
studied here and those reported by Chabchoub et al. (2008) and de Leew et al. (2008) as shown in
Figure 1.2. It was suggested by the latter authors that XPO1, KIAAA1841 and ubiquitin specific
peptidase 34 (USP34) may be linked to the multiple anomalies observed in all patients with a
del(2)(p15-16.1), due to the expression of XPO1 in multiple tissues, brain specific expression of
KIAAA1841 and role of ubiquitins in ID syndromes and autistic disorders. Of note, the OTX1
gene was not deleted in the cases reported by Chabchoub et al. (2008) and de Leew et al. (2008).
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The above findings strongly suggest that the 2p15-16.1 region is unstable and that a large
contiguous deletion of critical genes leads to a recognizable syndrome. To determine the
frequency of 2p15-p16.1 deletions, 798 individuals with an ASD and 186 controls were tested for
the presence of microdeletions similar to those described in Subjects 1 and 2. No additional cases
of either a deletion or duplication were found. Furthermore, no similar cases have been reported
from 5 other published studies (Christian et al., 1999; Jacquemont et al., 2006; Sebat et al., 2007;
Szatmari et al., 2007; Marshall et al., 2008) with at least 3410 subjects with an ASD evaluated for
subchromosomal CNVs. Thus, the 2p15-16.1 microdeletion has a prevalence of less than 1/2104
among individuals with an ASD. A microdeletion involving chromosome 2p16 but mapping 5.5
Mb distal to the deletions described herein was noted by the Autism Genome Project Consortium
(Szatmari et al., 2007). The neurexin gene within the involved 2p16 deletion reported in two
siblings with an ASD was further studied in 1181 families but association to ASDs did not meet
statistical significance (Szatmari et al., 2007).
Although no additional cases of 2p15-16.1 deletions were found in the ASD cohort, the
identification of these two other cases with smaller deletions in this region helps to reduce the
number of candidate genes that could be involved in the complex phenotypes. While the
overlapping 2p deletion region observed in subjects reported by Rajcan-Separovic et al. (2007)
and others (Chabchoub et al., 2008; de Leeuw et al., 2008) likely accounts for their common
syndromic findings, potential autism genes likely lie in the region discordant from that defined in
the 2008 publications. Thus, of the 40 genes and hypothetical proteins deleted in Subjects 1 and
2, the number of potential autism genes has been reduced to the 8 that were outside of the region
of overlap (Figure 1.2): hypothetical protein FLJ13305, CCT4, COMMD1, LOC388954,
B3GNT1, TMEM17, EHBP1, and OTX1. The OTX1 gene was selected as being of greatest
interest for candidate gene testing. It encodes a protein that acts as a transcription factor involved
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in forebrain and sensory organ development (Acampora et al., 1999). Sensory impairments are
common in individuals with autism. As previously noted, Leekam et al. (2007) found that
individuals with autism had a significantly higher mean score than those in the control groups and
that over 90% of all subjects presented with symptoms in more than one sensory domain. In
addition, the cortical migration defects detected by MR neuroimaging in both of our 2p-deleted
subjects, made OTX1 the best target for initial candidate gene screening. Despite the potential role
of OTX1 in the neurodevelopmental abnormalities in the two subjects described herein, familybased testing did not provide any evidence for an association between OTX1 and ASD in either
single marker or haplotype analyses.
It is interesting to note that OTX1(-/-) knockout mice described by Acampora et al. (1996)
manifest seizures, which are observed in ~30% of ASD cases. Heterozygotes, OTX1(+/-), showed
no signs of epilepsy. Subjects 1 and 2 do not present with clinical epilepsy, although EEG testing
suggested a heightened predisposition to seizures in Subject 2. As only one of two copies of the
gene is deleted based on the observed hemizygosity of the 2p15-16.1 region, based on the mouse
studies, I speculate that hemizygosity of OTX1 would not have a sufficient effect on its
expression to cause epileptic symptoms. Preliminary testing of a small number of individuals with
epilepsy from this cohort (n=32) did not show any significant association with markers in OTX1.
However, association testing on a larger group of individuals with both autism and epilepsy is
recommanded.
The absence of additional cases of 2p15-16.1 deletion in our own ASD study cohort
(n=798) and at least 3410 cases from other published studies (Jacquemont et al., 2006; Sebat et
al., 2007; Szatmari et al., 2007) suggests that the involvement of 2p15-16.1 with autism is absent
or a rare event. Given that the presence of dysmorphic features represents an exclusion criterion
for several ASD studies, this may reduce the frequency of detection of 2p15-16.1 deletions in
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other screening studies. The findings in the present study suggest that the newly described 2p1516.1 microdeletion syndrome represents a somatic and neurodevelopmental phenotype inclusive
of autism/autistic features, moderate to severe ID, CNS dysmorphology, and distinctive
craniofacial dysmorphology, rather than serving as a significant genomic and etiologic
contributor to autism.
4.4.3 Dup(7)(q11.23)
Subject 3 had a duplication of the WBS critical region and met the DSM-IV diagnostic
criterion for autism. The breakpoints of this 7q11.23 duplication were localized within the lowcopy repeats that predispose individuals to the genomic instability underlying the deletion
observed in the majority of WBS cases. This was also true for cases reported by Depienne et al.
(2007), Orellana et al. (2008) , one of the two cases reported by Kriek et al. (2006), and 6 of the 7
cases reported by Berg et al. (2007). The identification of a single case of dup(7)(q11.23)
amongst 798 individuals with ASD confirms the conclusion drawn by Depienne et al. (2007), that
duplication of the WBS region is a not a common cause of ASDs. Based on total numbers of
subjects with confirmed ASD which have been screened (Christian et al., 1999; Jacquemont et
al., 2006; Depienne et al., 2007; Sebat et al., 2007; Szatmari et al., 2007; Marshall et al., 2008),
including the present study, the frequency of dup(7)(q11.23) in individuals with an ASD or
autistic behaviours is therefore estimated to be, at most, approximately 1/1450.
My results are also in accordance with those of Berg et al. (2007) who suggested that
several patients with dup(7)(q11.23) may first be assessed for an ASD but may ultimately not
meet standardized diagnostic criteria. They reported 6 individuals with a typical, and one patient
with an atypical duplication of the WBSCR, two of which presented with behavioural symptoms
suggestive of ASDs (described as “ASD behaviours”). These individuals have challenges with
adaptive and social functioning complicated by severe expressive language deficits and, in
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Subject 2, severe verbal apraxia. The authors concluded that, in addition to speech impairments,
persons with dup(7)(q11.23) often present with several minor dysmorphisms. Common
dysmorphologies have not thus far been delineated. However, there is an overlap of some
craniofacial features observed in Subject 3 with previous reports, revealing relatively non-specific
physical anomalies, such as high nasal root, long nasal tip, short philtrum and thin lips. It is
possible that duplications of the WBS critical region will be recognizable in the future by their
distinctive neurodevelopmental/behavioural phenotype, which includes intellectual disability and
significant speech and language impairment, including verbal apraxia, and in some cases, ASD.
Three candidate genes (CYLN2, STX1A and GTF2i) were selected for association testing
with ASDs, based on their function during neurodevelopment and their location within the WBS
critical region, which is duplicated in Patient 3 and those reported by others (Figure 3.3). Singlemarker FBAT in the CYLN2 genes did not support an association with ASD susceptibility in the
multiplex or simplex families (P = 0.32 to 1.00). There was a modest over-transmission of the
GTCC haplotype in multiplex families (P = 0.040) but this was not significant after FDR
correction for multiple testing. Haplotype TDT of STX1A markers provided no evidence for
association with ASD in either cohort tested. An over-transmission of one allele (rs1569061-T) in
the STX1A gene was initially observed in multiplex but not in simplex families in single-marker
testing but this was, however, not significant after FDR correction was applied. Based on the
potential link between STX1A and epilepsy, it is possible that a modest association could be found
in the future by testing a large cohort of individuals with ASD and epileptic seizures. Preliminary
testing of a small number of individuals with epilepsy from my cohort (n=32) did not provide any
evidence to support this hypothesis. Interestingly, Torniero et al. (2008) found that the frequency
of duplication of the WBS critical region was nearly four times higher in patients with abnormal
neuronal migration and/or epilepsy (2/134; 1.5%) than in those without (2/510; 0.4%).
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Of the three candidate genes examined in 7q11.23, GTF2i proved to be the most
interesting. There was strong over-transmission of the common alleles for two of the three SNPs
tested in multiplex families (rs4717907-A and rs13227433-G: P = 0.0010 and 0.0032,
respectively; Table 3.15) and in the combined cohort (P = 0.039 and 0.041, respectively) but not
the simplex families. After FDR correction, the association remained significant in multiplex
families. The power of single marker analysis is limited by the fact that LD information contained
in flanking markers is ignored and haplotype testing therefore provides a more powerful and,
likely, accurate tool for association studies. Including only the two markers that were associated
with autism in the single-marker TDT (rs4717907 and rs13227433) yielded results that were
significant in the multiplex and combined cohorts but not in simplex families (increased
transmission of the AG haplotype; P = 0.0005, 0.013 and 0.443, respectively). The association
was no longer significant in the combined group after FDR correction. By including all three
markers in the GTF2i gene, over-transmission of the AGA haplotype (rs4717907, rs13227433,
rs2718270) was found in both the multiplex and combined cohorts (P = 0.0007 and 0.041). It was
not significant in the combined group after FDR corrections. These findings thus suggest that the
AG (rs4717907 and rs13227433) and AGA haplotypes (rs4717907-rs13227433-rs2718270) may
be associated with mutations or other variants that contribute to some elements of the ASD
phenotype in a sub-set of families with ASDs. This is a particularly important finding. As
mentioned, ASDs are highly heterogenous disorders, and frequently, when the cohorts tested have
been recruited using slightly different criteria or from different regions of the world, the findings
are not replicable. Strikingly, here, the families were recruited using different strategies by
different organizations. Some multiplex families were recruited by AGRE and the others, in
addition to the simplex families were self-identified and recruited on-line by ASD-CARC
(www.AutismResearch.ca). This strengthens my conviction that this association is real.
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Thus, of the three genes tested, GTF2i is the most likely candidate gene for ASDs. It is
located in the short region of overlap between the WBS critical region, duplicated in the patient
reported by Depienne et al. (2007), and the region deleted in a subject with ASD (Edelmann et
al., 2007) (Figure 3.3). While hemizygosity of GTF2i usually results in the loquacious personality
and relative strength in language exhibited by individuals with non-autism-associated WBS, its
duplication or altered expression (in some cases due to at least two copies of a mutant form of this
gene, resulting from non-homologous recombination between sister chromatids) could contribute
to the more severe social and expressive language deficits found in many individuals with ASDs.
Four isoforms (splice variants) of the TFII-I transcription factor encoded by GTF2i are
involved in various phases of development. They exhibit similar DNA binding characteristics and
their expression levels vary in different tissues, suggesting that their functions are non-redundant
(Cheriyath & Roy, 2000). The γ-isoform is predominantly expressed in neuronal cells (Perez
Jurado et al., 1998). Several pathways are regulated by the different isoforms of the TFII-I
transcription factor and disruption of one or more of these pathways could lead to autism or an
autism-related endophenotype. For example, Caraveo et al. (2006) showed that TFII-I reduces the
Ca2+ channeling activity of the transient receptor potential channel 3 molecule (encoded by the
TRPC3 gene). It is known that mutations of voltage activated L-type Ca2+ channels have been
associated with autism (Splawski et al., 2004). Also, TRPC3 is mainly expressed in the brain
before and shortly after birth, and is activated in response to brain-derived neurotrophic factor
(BDNF), a growth factor involved in the modulation of neurotransmitter release and synaptic
plasticity (Li et al., 2005). Therefore, I speculate that alterations in the expression or function of
TRPC3 by a mutated or haploinsufficient GTF2i could lead to autistic behaviours.
The recurrence of genomic rearrangements of chromosome 7q11.23, combined with the
results of my association studies indicates that although duplications of the WBS critical region
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are a not a common cause of ASDs, GTF2i may play a role in the etiology of a subgroup of ASD
cases. To verify this possibility, the re-sequencing of all 35 exons of GTF2i and nearly 1 kb of
promoter was carried out in 7 individuals from 7 families that matched a group of criteria,
(established based on the TDT results) in order to maximize the odds of identifying ASDassociated coding variants (families listed in Table 3.17). Since the association was obtained in
the multiplex group, individuals from families with more than one affected individual were
prioritized. Given that FBAT measures the over-transmission of an allele or haplotype from
heterozygous parents to affected offspring, only individuals whose two parents were
heterozygous for the AGA haplotype were selected. Similarly, all affected family members
needed to be homozygous for the AGA haplotype and had therefore received a copy of the AGA
haplotype from each parent, although an exception was made for family 40816 in which two of
three probands were homozygous and the third was a paternal half-sibling. Finally, families were
excluded if unaffected siblings were homozygous for the AGA haplotype. When this was done,
no sequence variants were found in any of the 7 individuals tested. Despite the result that no
novel mutations were identified in the small subgroup of individuals tested, the association results
presented herein remain strongly suggestive that GTF2i plays a role in the etiology of ASDs.
Van der Aa et al. (2009) recently reported a group of 14 individuals with a duplication of
the WBS critical region. These were identified in a cohort of patients with idiopathic MR and
variable speech delay. Three patients had a formal autism diagnosis and three others showed
autistic features. Of the 27 individuals with a dup(7)(q11.23) reported in the literature so far,
speech and developmental delays were observed in 27 and 24 cases, respectively, while autism or
autistic features were reported in 11 patients. As well, a group of facial features is commonly
observed in patients with a 7q11.23 duplication. These include a thin upper lip (75%), high broad
nose (58%), straight eyebrows (54%) and short philtrum (50%), amongst others. I suggest that,
133
while other genes on the WBS critical region may account for these features, GTF2i represents
the most likely gene for ASD-associated symptoms.
4.5 Conclusions and recommendations
The development of array-based technologies has allowed the high-resolution detection of
several rare novel CNVs in individuals with ASD, thus leading to the identification of many new
candidate loci for these disorders. However, the identification of genomic alterations of unclear
significance can create significant counselling difficulties for clinical geneticists. A more
complete understanding of the role of specific CNVs in ASD will require thorough phenotypic
analyses of several cases with similar rearrangements. Because of our knowledge of the genetic
code, it is possible to determine whether a nonsense or missence change arises from a newly
discovered point mutation. Similar rules or standards for determining the relevance of CNVs to a
given phenotype do not exist and need to be established. This represents a daunting task, as it is
difficult to predict the penetrance of CNVs with our current understanding of human genetics.
Time-consuming expression-level experiments need to be carried out for each CNV of interest to
confirm their effect on gene expression and our current capacity to discover novel chromosome
abnormalities far outweighs our capacity to characterize their effect.
In this thesis, three genomic abnormalities detected in patients with an ASD were refined to
localize new autism candidate genes for association testing. My findings suggest that the 2p15-16.1
microdeletion detected in 2 of 798 individuals screened likely contributes to a somatic and
neurodevelopmental phenotype inclusive of autism and/or autistic features, distinctive craniofacial
dysmorphology and moderate to severe ID. This phenotype was not caused by OTX1 since no
evidence was found for an association between this gene located in this region and ASDs. Other
groups have reported deletions on chromosome Xp22 similar to the one described herein. I found
134
no evidence for association between either NLGN4X or VCX with ASD although other authors
identified coding variants in the former that suggested its involvement in these disorders. Finally,
the duplication of the WBSCR pointed to GTF2i as a potential candidate gene for ASD and
subsequent association testing and statistical analyses supported this hypothesis. Although novel
coding variants were not identified in the 7 individuals tested, I suggest that future screening of a
larger number of individuals could result in the uncovering of ASD-associated mutations.
The results of this project confirm that the characterization of genomic abnormalities
detected in individuals with ASD provides invaluable information to localize new candidate
genes for these disorders. The screening of nearly 800 individuals with an ASD for the presence
of additional cases of the rearrangements described herein confirmed that, although the study of
CNVs is a fruitful approach for the identification culprit genes for ASD, the majority of autism
cases are not caused by genomic rearrangements.
It is important to note that the interpretation of CNV studies results is complicated by the
fact that the mode of inheritance of ASDs is still unknown. Data from family and twin studies
suggest a multigenic model, likely involving several genomic loci. Genomic abnormalities such
as the ones reported herein and by others may represent autism-susceptibility alleles which, in
combination with other, yet to be identified, risk factors (genetic or environmental), predispose to
ASDs.
CNVs are a predominant source of genetic variation on the human genome (Eichler, 2006;
Sebat et al., 2004) and are believed to drive evolution through exon shuffling and gene
duplication (Hurles, 2004; Jiang et al., 2007; Zhang et al., 2009). Indeed, more than 25% of
human genes represent CNVs in one or more primate species, with gains outnumbering losses
with a 2.3:1 ratio (Dumas et al., 2007). High-resolution screening studies often report deletions in
135
multigene families, commonly involved in immune response, sensory perception, cell adhesion
and signal transduction (Sebat et al., 2004; Tuzun et al., 2005; Conrad et al., 2006). Similarly,
other members of these multigene families are also enriched in segmental duplications (Bailey et
al., 2002), supporting the central role of CNVs in evolution. However, CNVs can lead to diseases
or disorders and are, therefore, exposed to negative evolutionary pressure. This hypothesis is
supported by the fact that intragenic SNPs (located either in coding sequence or within introns)
are underrepresented in recurrent deletion regions compared with the rest of the genome,
reflecting the hypothesis that genic deletions are deleterious and subject to purifying selection
(Conrad et al., 2006). Moreover, CNVs are preferentially located outside of gene-rich genomic
regions (Redon et al., 2006). This may explain why, while deletion polymorphisms may have an
important role in the genetics of complex disorders such as ASDs, they are not observed in a
majority of affected individuals. Given the low frequency of most genomic abnormalities
reported to date, this means that large family sets will likely be necessary. Indeed, while CNVs
were found in 10% of individuals with idiopathic ASD compared to 1% in controls, only a
handful were reported in more than one case (Sebat et al., 2007). Interestingly, CNVs were found
in only 3% of patients with an affected first-degree relative (Sebat et al., 2007). In this study, a
large proportion of the cases screened for deletions and duplications were from multiplex
families, which may have reduced the odds of identifying additional cases.
Future screening studies should include an increased number of simplex families. For an
autism-associated rearrangement to be found in a multiplex family, that family would have to
satisfy one of the following scenarios:
1) The abnormality occurred de novo in each affected sibling, which is unlikely even in the
case of recurrent LCR-mediated rearrangements.
136
2) The abnormality was inherited from a healthy parent – this suggests an incomplete
penetrance of the CNV or an interaction with one or more additional loci.
3) The abnormality was inherited from an affected parent – there are examples in the
literature and in the ASD-CARC family registry of patients born to at least one parent
with mild or moderate intellectual and/or social impairments. In general, the parent does
not reach ASD diagnostic criteria but presents with autistic behaviours.
Multiple-incidence families are expected, however, to represent an invaluable tool for
association testing as they are expected to account for a lower ratio of sporatic/familial (ie:
inherited) cases. In this thesis, a significant association of SNPs in one gene, GTF2i, with ASD
was observed in multiplex but not in simplex families, supporting the important role of the former
in association and linkage studies. Thus, simplex families offer great potential for the discovery
of novel, sporadic, CNVs purpoted to signal genetic segments containing one or more ASD
gene(s). Multiplex families, on the other hand, should allow the identification of these ASD genes
and, potentially, lead to the uncovering of specific ASD-causing mutations.
It is crucial that the number of available DNA samples be increased to ensure that subtle,
but important, genetic variations will be uncovered. As time passes, there is an increased
availability of ASD samples available for testing as research organizations such as ASD-CARC
have been working diligently over the years toward increasing sample numbers. Availability of a
very large number of patients, both from simplex and multiplex families, is critical for future
advancement of autism research. This represents a great difficulty given the time-consuming
approaches used to pose an ASD diagnosis and to obtain phenotypic information that will be
invaluable for subgrouping efforts. Improved phenotyping and more accurate diagnostic methods,
137
combined with a multidisciplinary assessment of patients are necessary to ensure success in the
quest to identify genetic factors for specific subtypes of ASD.
Technological advances over the next decade will likely increase the productivity of
genetic research. Genotyping capacity is rapidly increasing as the cost of microarrays-based
experiments is rapidly decreasing. Whole-genome association studies, which were cost- and
labour-prohibitive even a few years ago, are emerging in the literature and have already been
shown to be effective at identifying common alleles of small effect associated with complex
genetic disorders including bipolar disorder, coronary artery disease, type 1 and 2 diabetes,
Crohn’s disease and rheumatoid arthritis (Hirschhorn & Daly, 2005; Scott et al., 2007; Wellcome
Trust Case Control Consortium, 2007; Zeggini et al., 2007). Large-scale sequencing technologies
are also evolving at a rate that shows great promises for the future of gene discovery. New
techniques allow for hundreds of megabases of sequence to be obtained in a single run, and the
$1000 genome (Service, 2006) proposed a few years ago does not seem unattainable anymore.
Finally, although several genes such as GTF2i likely play a role in the aetiology of ASDs,
genetics is not sufficient to fully explain inheritance patterns. Environmental factors, such as viral
or bacterial infections and exposure to toxic substances may also contribute to the disorders. It is
only when the interactions between the different risk factors are understood that the aetiology of
ASD will truly be uncovered.
Thus, although optimism is warranted regarding gene discovery in ASD, it is likely that
multi-disciplinary cooperation, combined with diligence, will continue to be required to truly
understand and solve the puzzle of autism.
138
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