GENOMIC REARRANGEMENTS IN DISORDERS: IDENTIFICATION OF NOVEL CANDIDATE

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 testing. I molecularly characterized two overlapping 2p152p16.1 deletions detected in unrelated individuals with confirmed autistic disorder (Subject 1) or autistic features

(Subject 2), a 1.4Mb on Xp22 (Subject 1) and a duplication of chromosome

7q11.23, reciprocal to the WilliamsBeuren Syndrome (WBS) deletion, in one individual with an

ASD (Subject 3). Using realtime semiquantitative 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, Xlinked ( VCX ), Neuroligin 4, Xlinked ( NLGN4X ), Syntaxin 1A

(STX1A ), Cytoplasmic linker 2 ( CYLN2 ) and General 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 brainexpressed 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 coauthors 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).

• ArrayCGH experiments were performed by Dr Ying Qiao and Chansonette Harvard, under

the supervision of Drs ME Susanne Lewis and Evica RajcanSeparovic (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 coauthorship of the Monash Graduate

School; http://www.monash.edu.au/phdschol/forms/pginfo/coauthor.rtf)

iv

To my brother Nicolas.

v 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 ASDCARC 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 RajcanSeparovic 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 CIHRIHRT grant (#43820) to JJAH, an OMHF grant to JJAH, a CIHR grant (RT64217;

PI: MESL, MOP 74502; PI: ERS), Michael Smith Foundation for Health Research (MESL and

ERS), and ongoing 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 highrisk 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 Genomewide 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 Casecontrol association ...... 12 1.3.2.2 Familybased association...... 13 1.3.2.3 Results from candidate genes studies...... 15 vii 1.3.2.4 Genomewide 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)(p15p16.1) and del(x)(p22.2p22.3)...... 36 1.5.2 Subject 2 with del(2)(p15p16.1)...... 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

viii 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 Realtime semiquantitative 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 ...... 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)(p1516.1)...... 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 Familybased Association Tests...... 102 3.3.4 GTF2i resequencing ...... 105

CHAPTER 4 Discussion...... 110

4.1 Choice of experimental procedures and statistical analysis...... 110 4.1.1 Realtime sqPCR – sensitivity and specificity...... 110 4.1.2 Familybased vs casecontrol association...... 111 4.1.3 Correction for multiple comparisons ...... 112

4.2 The 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)(p15p16.1) ...... 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 genomewide 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 Familybased association testing of single markers within the NLGN4X and VCX genes with autism...... 85 Table 3.5 Familybased haplotype association for the markers in the NLGN4X gene in families with ASD...... 86 Table 3.6 Familybased 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 Familybased association testing of single markers within the OTX1 gene with autism...... 95 Table 3.11 Haplotype familybased 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 Singlemarker familybased association testing for the markers in the STX1A, CYLN2 and GTF2i genes in families with ASD...... 103 Table 3.16 Familybased 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 ...... 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 critical region on chromosome 7q11...... 48 Figure 3.1 Refinement of the breakpoints of the deletions on chromosome Xp22 in Subject 1 using realtime sqPCR...... 80 Figure 3.2 Refinement of the breakpoints of the deletions in Subjects 1 and 2 using realtime 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 5HT Serotonin (5hydroxytryptamine) 5HTR3A 5HT receptor 3A 5HTT 5HT transporter AChE Acetylcholinesterase ACR Acrosin ADHD Attentiondeficit hyperactivity disorder ADIR Autism Diagnostic InterviewRevised ADOSG Autism Diagnostic Observation ScheduleGeneric AGRE Autism Genetic Resource Exchange AMY1A Amylase alpha 1A ARSE Arylsulfatase E AS ASDCARC ASD – Canadian American Research consortium ASD Autism Spectrum Disorder B3GNT1 UDPGlcNAc:betaGal beta1,3Nacetylglucosaminyltransferase 1 BAC Bacterial Artificial Chromosome BDNF Brainderived neurotrophic factor BZRAP1 Benzodiazapine receptor (peripheral) associated 1 CACNA1G Calcium channel, voltagedependent, 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 Copynumber polymorphism CNTN4 Contactin 4 CNTNAP2 Contactinassociated proteinlike 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 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 PSD952) DSMIV Diagnostic and Statistical Manual of Mental Disorders 4 th Edition DZ Dizygotic EHBP1 EH domain binding protein 1 EN2 Engrailed 2 EV Euchromatic variant FANCL Fanconi anemia, complementation group L FBXO40 Fbox protein 40 FDR False discovery rate FISH Fluorescence in situ hybridization FMR1 FRX Mental Retardation 1 FRX Fragile X FWER Familywise error rate FXTAS FRX Tremor and Ataxia syndrome GABA γaminobutyric acid GABRA5 GABA receptor subunit A5 GABRB3 GABA receptor subunit B3 GABRG3 GABA receptor subunit G3 GKAP Guanylate kinaseassociated protein GPR143 G proteincoupled receptor 143 GSTM1 Glutathione Stransferase M1 GTF2i General transcription factor II, i GWAS Genomewide association study IBD identicalby descent xv IBI Intensive behavioral intervention ICD10 International Classification of Mental and Behavioral Disorders 10 th revision ID IGHD Immunoglobulin heavy chain D IQ Intelligence quotient KAL Kallman syndrome LCR Lowcopy 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 methylCpG binding protein 2 MLPA Multiplex ligationdependent probe amplification MLS Multipoint maximum LOD score MPX Multiplex MRI Magnetic resonance imaging MTNR1B Melatonin receptor 1B MZ Monozygotic NAHR Nonallelic homologous recombination NF1 Neurofibromatosis type 1 NLGN Neuroligin NLGN2 Neuroligin 2 NLGN3 Neuroligin 3 NLGN4 Neuroligin 4 NSF Nethylmaleimidesensitive factor OTX1 Orthodenticle homolog 1 PARK2 Parkinson disease 2 xvi PDD Pervasive Developmental Disorders PDDBI PDD Behavior Inventory PDDNOS PDD – not otherwise specified PET Positron emission tomography PEX13 Peroxisome biogenesis factor 13 PKU Phenyketonuria PWS Prader Willi syndrome RABL2B RAS oncogene familylike 2B RAI1 Retinoic AcidInduced 1 REL vrel reticuloendotheliosis viral oncogene homolog (avian) RELN Reelin RFWD2 Ring finger and WD repeat domain 2 RFLP Restrictionfragment length polymorphism RTPCR 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 SmithMagenis syndrome SNAP25 25kDa synaptosomeassociated protein SNARE Soluble NSF Attachment Protein Receptors SNP Single nucleotide polymorphism sqPCR Semiquantitative 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 Vesicleassociated membrane protein 2 VCX Variable charge, Xlinked VCY Variable charge, Ylinked WBSCR WilliamsBeuren syndrome critical region XLI Xlinked 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 nonverbal 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 4 th Edition (DSMIV) (American Psychiatric Association, 1994) and

International Classification of Mental and Behavioral Disorders 10 th revision (ICD10) (World

Health Organization, 1992). PDD include, in addition to autistic disorder, Asperger syndrome,

Rett Syndrome, pervasive developmental disorder – not otherwise specified (PDDNOS) 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 abovenormal range. They do, however, have

severe social deficits and exhibit repetitive behaviours. Individuals with PDDNOS, on the other 1 hand, also present with impairments in social interactions but the severity of the symptoms does not reach the “autismcutoff” 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 methylCpG 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 ICD10 and DSMIV 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 behaviourbased, 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 InterviewRevised (ADIR) (Lord et al. , 1994), a questionnaire to be answered by the parents or caregivers of affected individuals, and the Autism Diagnostic Observation Schedule

Generic (ADOSG) (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 PDDNOS, 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; YearginAllsopp 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; YearginAllsopp 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. BaronCohen (2006) reported

that individuals with Asperger’s syndrome and high functioning autism appear to present with an

overdeveloped systemizing approach to the world and are likely to choose rulebased 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 (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; JuulDam 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 cooccur 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; YearginAllsopp 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 PDDNOS 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 Xlinked 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

nonverbal 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 cooccuring with ASD. While individuals with ASD cannot also be

diagnosed with attentiondeficit hyperactivity disorder (ADHD), as the two are mutually

exclusive in the DSMIV, 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 cooccurring conditions such as short attention

5 span, hyperactivity, anxiety, aggressiveness, sleep problems and selfinjurious behaviours

(Findling, 2005; Witwer & Lecavalier, 2005). In some cases, this improves the effectiveness of

IBI programs by decreasing interference with treatment of comorbid symptoms. Alternative approaches are also commonly used but there is little to no evidence of their effectiveness. Treatment is usually multidisciplinary, including speech and language 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 , 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 probandwise concordance rate of 53% (pairwise 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 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

GenotypePhenotype Consortium, 1993).

Obvious chromosome abnormalities account for ~5% of ASD cases (WeidmerMikhail et

al. , 1998; Lauritsen et al. , 1999; Konstantareas & Homatidis, 1999) and an additional ~5% arise

from brainaffecting 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) Genomewide 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 ASDcausing 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 co segregation 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 crossingover 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 log 10 of this ratio. LOD scores provide a statistical estimation of the co segregation 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 Nonparametric linkage analysis (allelesharing method)

The allelesharing 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 χ 2 test.

Nonparametric 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 wholegenome 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 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 metaanalysis of 6 independent genome scans (Trikalinos et

al. , 2006), harbours chromosome abnormalities in some individuals with ASD. A preliminary

wholegenome 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 followup study with a total of

125 siblingpairs 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

(AshleyKoch 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). Casecontrol (or populationbased)

association study is based on measuring allelic association between a marker and diseasecausing

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 in four ethnic populations

(International HapMap Consortium, 2003). This allows the costefficient assessment of much of the common haplotypic variation within a population at a given locus.

1.3.2.1 Casecontrol association Casecontrol (or populationbased) 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 χ 2 test is used for determining the statistical significance of allele distribution

in the two groups.

12 Subject recruitment for populationbased association studies is easier than for linkage and familybased 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 populationbased studies, as population stratification is strongly detrimental to highquality 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 Familybased association The problem of population stratification can be overcome by using a familybased

association method, which uses nontransmitted 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 family based association testing. It tests for distortion in the transmission of alleles from heterozygous parents to affected child. The χ 2 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 1-1 1-2

1-2 1-1

1-2 2-2 2-2 1-2

2-2 1-2

Figure 1.1 Transmission disequilibrium test. Four nuclear families, each with an affected child, are illustrated. Homozygous parents (11 and 22 in this example) are not informative as heterozygous parents (12) are needed to compare the frequencies of transmitted vs non transmitted 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 χ 2 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 (5HT) 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 5HT 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 5HT 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 rigid

15 compulsive behaviour (Tordjman et al. , 2001; Mulder et al. , 2005). Other studies have examined alterations in the metabolism of 5HT that may be involved in serotonergic abnormalities. For example, studies on genes coding for MAOA, an enzyme involved in the catabolism of 5HT, reported correlations between a lowactivity 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 5HT 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 5HT may be important in the etiology of ASDs. Tryptophan hydroxylase 1 ( TPH1 ) encodes the ratelimiting enzyme in the synthesis of 5HT 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 γaminobutyric 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 neurexinneuroligin 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; AshleyKoch 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 5

HT pathway genes for linkage and association in 403 ASD families. They genotyped 45 SNPs

and found association of one within the 5HT receptor 3A ( 5-HTR3A ) gene on .

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 5HT 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 subgroups 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 subgroup of individuals with autism and developmental regression but these finding was not replicated in a followup study (Parr et al. ,

2006).

1.3.2.4 Genomewide association studies

Genomewide association studies (GWAS) may provide a more powerful approach to identifying ASD susceptibility genes. Similarly to casecontrol 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 cost prohibitive and technically challenging but GWAS reports have now begun to emerge in the literature. 18 Using genomewide association in 72 multiplex families, Arking et al. (2008) identified a common variant in the Contactinassociated proteinlike 2 ( CNTNAP2 ) gene associated with ASD in malemale 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 438family discovery dataset and a 487family 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 from the cadherin superfamily, which mediates calciumdependent cellcell adhesion.

Based on previously reported evidence of linkage of chromosome 17q11q21 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, voltagedependent, T type, alpha 1G subunit

(CACNA1G ) gene to be associated with ASD at a regionwide significant level. The two SNPs,

located in intron 9 of CACNA1G were in strong LD (r 2 = 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 voltagegated calcium channels had previously been linked to ASDs (Splawski et al. , 2004; HemaraWahanui et

al. , 2005; Krey & Dolmetsch, 2007), CACMA1G was considered as the strongest candidate gene

for these disorders on the 17q11q21 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 autismcausing 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 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 highthroughput sequencing and screening

20 technologies. Such rearrangements range from large cytogenetic abnormalities (>1 Mb) to single 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 singlebase 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 10 6 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 10 6 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 lowcopy repeats (LCR), are segments of DNA, 1500 kb in size, with

9099% 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

and interspersed nature, LCRs serve as substrates for structural

rearrangements through nonallelic 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, Smith

Magenis 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 AcidInduced 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 arraybased approaches, allow the screening of the genome for submicroscopic genomic abnormalities. One of the first largescale studies lead to the identification of 76 loci associated with copynumber 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 highdensity SNP arrays, which are based on the computational analysis of genotyping errors and nonMendelian 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 Stransferase 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 arraybased 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 15q11q13 were found in 6/9

(67%) mothers of patients with Angelman syndrome associated with a maternal deletion of

15q11q13. This compares to only 9% in the general population (Gimelli et al. , 2003). Similarly, one in three parents who transmitted a deletion of the WilliamsBeuren 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 pairedend 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 copy

number 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 cytogenetically

visible extra euchromatic material (Ritchie et al. , 1998).

1.3.5 Effects of structural variations Studies using BAC comparative genomic hybridization (CGH) arrays reported that 800

1800 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 ~1.5fold 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 αdefensin 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 αsynuclein 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 dosagesensitive nature for several of these genes and reflecting their role in the regulation of transcription. Genes associated with environmentallyregulated 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). Crossspecies 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

nonfunctional, 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 microscopybased karyotyping techniques is approximately 35 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 genomewide 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 highresolution

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 pairedend 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 genomewide 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 genomewide 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.

Pairedend mapping Detection of balanced Requires highdensity rearrangements sequence data Larger insertions are not detected Very costly

Comparative (crossspecies) Detection of balanced Requires sequence data sequence analysis rearrangements 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 VeenstraVanderweele 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 autismsusceptibility using microsatellite markers at ~10 cM intervals in 105 multiplex families, they identified four interstitial deletions, two on and two on , 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 copynumber polymorphisms

(Iafrate et al. , 2004), the unexpected identification of microdeletions in this linkage study paved the way for further CNVscreening 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 arrayCGH. 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 arrayCGH 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 copynumber 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 & RosenSheidley, 2001). Recent studies have reported that the proportion of de novo CNVs differs between simplex families (710%), multiplex families (23%) 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 15q11q13, most commonly in the form of a supernumerary isodicentric chromosome or, less frequently, from an interstitial duplication (Folstein & RosenSheidley, 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 autisticlike symptoms in a subset of cases (Maestrini et al. , 2000). Duplications of 15q11q13 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 15q1113 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). Paternally derived 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 paternallyderived triplications are associated with a more severe phenotype that includes developmental delay, language deficits, epileptic 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 22q11q13, 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 , which affects males and females equally, is associated with developmental delay, speech impairments, facial dysmorphologies and autisticlike 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 familylike 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 wellestablished associations with ASD such as 15q11q13 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 Fbox 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 voltagegated 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 co segregates 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 nonrandomly 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 ASDrelated 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 CGHmicroarrays are ideal to identify candidate genomic regions. ArrayCGH 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 PHD containing protein, are now associated with the SMS 17p11 (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 microsatellitebased 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 RajcanSeparovic 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.

Realtime semiquantitative 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 copynumber 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 ASDassociated

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 Rajcan

Separovic et al. (2007). She is an 8yearold nonverbal female, born to nonconsanguineous, 23

and 26yearold Caucasian parents, with autistic disorder diagnosed by the highest measures

[ADOSG (Lord et al. , 2000) and ADIR (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 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 MillerDieker syndromes.

1.5.2 Subject 2 with del(2)(p15-p16.1) Subject 2 is a 6yearold, hearing impaired, nonverbal male with autistic features

subthreshold for a definitive ASD [ADOSG 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. 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 (20006000 Hz), as well as deficits in social interaction and attention. There was no 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 12yearold female meeting DSMIV criteria for an ASD and severe expressive language deficiency confirmed by ADOSG/ADIR (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 selfcare. 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 5070). 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 selfstimulatory 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 (>550 band resolution)

as well as negative Fragile X testing. 37 Growth parameters revealed height, weight and head circumference respectively at the 8 th ,

65 th and 50 th 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 2p15p16.1 (Rajcan

Separovic 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 2p1516.1 region have been reported in controls, including a recurrent 2.9 Mb deletion/duplication in normal individuals (58.261.1 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 RP11201J10 to

RP1144OP5; ~57.1 Mb60.7 Mb) was reported in the Decipher database

(https://enigma.sanger.ac.uk/perl/PostGenomics//; patient CHG00001375 and Dr Szuhai

38 K, personal communication). However, the patient did not resemble the two subjects studied here

(RajcanSeparovic et al. , 2007) and did not have an ASD. Additionally, recurring de novo

microdeletions of various extents have been detected within the described 2p1516.1 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 Exportin1

(XPO1 ), KIAAA1841 and USP34 may be linked to the multiple anomalies observed in all patients with a del(2)(p1516.1) 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 (RajcanSeparovic 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, nonautistic, 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

SubjectPatient 1 1

SubjectPatient 2 2

ChabchoubChabchoubet et al. al.

de Leeuw etet al.al. De Leeuw

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. Bicolored clones were found to be hemyzigous only in Subject 1.

40 ribosomal protein SA (p40) ( LOC388954 ), UDPGlcNAc:betaGal beta1,3N

acetylglucosaminyltransferase 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 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 OTX1

mediated 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 2pdeleted 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 4

42% 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 2p1516.1 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 Xlinked recessive chondrodysplasia punctata. Several genes map to Xp22,

including STS , deficiency of which causes Xlinked 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 βneurexins, promote the formation of synapses (Song et al. , 1999; Chih et al. ,

42 2004; Varoqueaux et al. , 2004). NLGNs are abundant in the postsynaptic membrane of glutamatergic ( NLGN1 , 3 and 4) or GABAergic (neuroligin 2; NLGN2 ) synapses where they associate with PDZdomain scaffolding proteins to regulate the glutamateGABA 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 AchEhomologous domain of NLGNs is known to alter ligandbinding 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 AchEhomologous domain in the first family, and a basepair causing a 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 2basepair deletion in NLGN4 in a large

French family. The deletion was present in all male family members affected by Xlinked mental retardation, autism or PDD but not in nonaffected family members or controls. The mutation caused a premature stop codon (429X), suppressing the transmembrane and dimerization domains, which are required for cellcell interactions.

Three missense mutations (G99S, K378R and V403M) in the AChEhomologous 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 neurexinneuroligin pathway. Neurexins are typeI 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 α and βneurexins from independent promoters. Hundreds of neurexin isoforms are generated through regionallyregulated 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. Inframe 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 PSD95) and guanylate kinaseassociated 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 (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 corticaldysplasia 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 genomewide linkage and familybased 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 neurexinneuroligin complex in individuals with ASD suggest a role for this pathway in the etiology these disorders.

1.7.2.2 VCX Northern blots and reversetranscriptase PCR (RTPCR) experiments revealed that all copies of Variable charge, Xlinked ( VCX ) and Variable charge, Ylinked ( 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

Xp22.222.3 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, , 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 downslanted corners

of the mouth. His hands were broad and short and clinodactyly of the fifth finger was observed.

The authors suggested that Xinactivation patterns might be responsible for the various phenotypes in the females. Indeed, nonrandom inactivation of the paternal Xchromosome 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 WilliamsBeuren

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 generallyaccepted 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

1.5–2.0 Mb FKBP6 FZD3 WSTF BCL7B WSbHLH STX1A CPETR2 CPETR1 ELN LIMK1 WSCR1 RFC2 EIF4H GTF2i HIP1 WSβTRP CYLN2

Cen Tel

Figure 1.3 Williams Syndrome critical region on chromosome 7q11. Purple blocks illustrate

segmental duplications (duplicons) that act as substrates for unequal crossingover 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 ASDlike 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 C terminal and a fourhelix 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

synaptobrevin2 (or vesicleassociated membrane protein 2; VAMP2), and plasma membrane proteins STX1A and SNAP25 (25kDa synaptosomeassociated 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 aminoacid carrier 1

(encoded by EAAC1 ) internalization and, consequently, in glutamate transport and conversion to

gammaaminobutyric 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 442% 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 5HT uptake assays, confocal microscopy and coimmunoprecipitation assays, that STX1A interacts with 5HTT to alter its subcellular localization, resulting in a reduction of 5HT 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 CAPGLY 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 longterm 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 ubiquitouslyexpressed multifunctional phosphoprotein, general

transcription factor IIi (TFIII), 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 TFIII transcription factor and disruption of one or more of these pathways could lead to autism or an autismrelated 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 realtime 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 (ASDCARC). 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 InterviewRevised

(ADIR) (Lord et al. , 1994) and/or PDD Behavior Inventory (PDDBI), which has been shown to have excellent reliability with the ADIR (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 ADIR algorithm, and who had an apparent ageofonset before 36 months. Those referred to as “not quite autism” met the ageofonset 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 ageofonset criterion. “Broad spectrum” generally includes individuals with

PDDNOS 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

ADIR 628 Autism 1 491 Not quite autism 55 Broad spectrum 82

PDDBI 52

ADIR, ADOS, Multidisciplinary Team assessment 2 118 1As 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 PDDNOS or Asperger syndrome.

2Referred by clinical geneticists following confirmation of an ASD diagnosis using goldstandard

ADIR, ADOS, and DSMIV 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 Xlinked 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 Realtime 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 nonpolymorphic 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 384well plates with concentrations of 900 nM for

each primer, 2 L (5 ng/L) DNA and 1X SybrGreen Mastermix (Applied Biosystems) in a 16

L 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 , C t, 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 Position on chromosome (kb) 2pScreen1 GGC TTT GCT CCT TCT CCT AGT TT 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.

Position of Probe Primer sequences chromosome (kb) XpProximal1 CCCGGGCCCCTTCAG 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.

Position on Probe Sequence chromosome (kb) XpDistal1 AAACACAGGCAAATGATGAGATATG 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.

Position on Probe Primer sequences chromosome (kb) 7qProximalA AGGAGTAGCACAAAGTCCTGCAT 71 674 TCGGAGGCTGCCCTAAGG 7qProximalB TTGGCCATATGCACAAGTAACTCT 71 724 CCAGGTGCAGACAGGATGCT 7qProximalC TCTTCCATCGCAGCAATAACAT 71 809 TGCATTCAGCTTTGCAGTCCTA 7qProximalD GGATCCTGTGCCCTATATGATTTC 71 872 CCAACTGCTCCACAGTAAACCA 7qProximalE AACAGCAAAGCCCAGATAAATGTAA 71 935 TGGGACCTCTCCTCACCTTTAA 7qProximalF CCACTCCTTCGATGAAACTCAGT 72 002 GAGCAGAAACCAGGGCAGAA 7qProximalG CTGCACAGTAAGCGGTTTGTG 72 048 TTGGTTGCTTGGATGGTGTTT 7qProximalH TGGGAGGGAATGAAACCAGAT 72 102 AAAGTCCCCATTCTTTTCCTTTTT 7qProximalI GCCCAGGTGCAGAGAGAGATA 72 187 AGGCTGCAGCAGGACAAGA 7qProximalJ CCTTCAAAGCCCCCAGCTA 72 253 AAACTCGGCAGAGTCTAAGATCCTT 7qProximalK 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 7qProximalL CCGACCCTGTCACCCAATAT 72 385 GCTTTGAGTGAAATGGGAGGTT 7qProximalM TGCCACAGTGTCCGGAAA 72 431 CTGCCCAGCAAGTGAACAAA 7qProximalN CACCCAATGACCAAGAAGCA 72 512 CCCCTTCTGTGGTTTTCACACT 7qProximalO CGCACTAGAGGGCAGTCTGAA 72 575 GCCTATCACGGGCGCTTA 7qProximalP GCCTCATTTCCTTCCCTTTTG 72 634 CACTGCCCACAGCTGAGTCA 7qProximalQ 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.

Position on Probe Primer sequences chromosome (kb) 7qDistalA CTGGCAGAGAAGACTGACCAAGT 73 355 GGTGCCCGGCTGTCTGT 7qDistalB TGTACCCCGTGAGTGTTCCA 73 380 CCCACCTCGGGCACTTC 7qDistalC GTATGGCCATGGGTGTGTCA 73 409 CGGAGCTGATGGAGGAACTG 7qDistalD CACGCTGTGCACACACTAACC 73 433 GGCCTCAGTTTCCCATCCAT 7qDistalE CTGGGTCTGTGGTTCAGCAA 73 465 GTGAGAAAACAGGAACAGATTGGA 7qDistalF CAGAGAAGGTGATAGGCAGAAAATG 73 487 CCACCTGCCCTGTGTACATCT 7qDistalG TGGTGAAGCCACTGTTGCA 73 515 GCCAGCCCTGTTCCGTTT 7qDistalH TCAGAATATTGTGCCTCCTCAAAC 73 540 GCTGAGGGCTCCATCTCTAGTC 7qDistalI TCCTGTGCCTGTCACTGAGTAAG 73 586 TCGTTTATCCTGAGGGCTTTTG 7qDistalJ AAGCTGGTGCCAACTCTCATG 73 642 TGTTGGCGTCATCTTCATCTACA 7qDistalK ACAGCAGGCTGGTATGAATGAA 73 702 TGACCACACTCTGGCTTGTACCT 7qDistalL 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 7qDistalM CCTTCAAAGCCCCCAGCTA 73 806 AAACTCGGCAGAGTCTAAGATCCTT 7qDistalN TGAAAACCATACTTGGCAATGG 73 861 AAGCCGAGGTGAAAATCGAA 7qDistalO TCCAGCTCTTTATTGGCCAAA 73 921 CCACCAATTCTGTCTCCTATGAAA 7qDistalP GGATGGCTGTTTTAATGATGAGAGA 73 975 CGAAAGCTGGGTTTCCCTATC 7qDistalQ TTCCTGGGCCGGATCTG 74 025 GACCCAGTGCAGGCCAAA 7qDistalR CCAGCAAAGATCTGGGCATT 74 081 GGGCGGTCCTGGAGATG

64 Table 2.7 Proximal 2p16 breakpoint refinement in Subjects 1 and 2.

Position on Probe Primers sequences chromosome (kb) 2pProximal1 CCC CGT GGA GCA CAA GTG 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.

Position on Probe Primer sequences chromosome (kb) 2pDistal201 CTG AAT GCC CTG AAT TAT TTG TTG 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.

Position on Probe Primer sequences chromosome (kb) 2pDistal101 CTG CTC CTT GTA AGC ACA TTT AGG 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 copynumber ratio was calculated. Cut

off 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 re tested 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’ SNPbrowser TM 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 L, 5 ng/L) 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 L 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 HardyWeinberg

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 regenotyping 70 or omitted from data analysis. LD between markers was determined using the 2LD program

(Zhao, 2004). Singlemarker and haplotype familybased association testing (FBAT) was carried out with FBAT v1.5.5. All familybased tests were performed under an additive model.

Allele and genotype frequencies in affected individuals were compared to those of the comparison group using χ 2 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 familybased 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…H n being the null hypotheses tested and P(1) …P (n) their corresponding P

m values, ordered in increasing order, one has to find the largest m such that P(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 96well plates with 5ng of DNA in a total reaction volume of 25 L. All PCRs were performed on a dyad MJ Research DNA Engine and 3

L of each sample were separated on a 1% agarose gel to assess the amplification reactions.

Primers for sequencing were diluted to 1.6 M 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 restrictionfragment 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 L. 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 MgCl 2 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 PCR conditions Sequencing primers

Annealing PCR [MgCl 2] Name Sequence Cycles Name Sequence temp. (°C) buffer 1 (mM) 941 Pro2F TCGGGGTTGTAAAGTTTTGG 55 or 60 2X 1,5 35 Pro2R ACCCCACATTCCTTGCAAAT to 475 bp Pro2R ACCCCACATTCCTTGCAAAT 499 Pro1F CGCAATTTGCAAGGAA 60 2X 1,5 35 Pro1R CCAGCGGGAGTTGTAGTTTC to 156 bp Pro1R CCAGCGGGAGTTGTAGTTTC 1 E1F GAAGCTGGGAGAGCAGAGAA 60 1X 1,5 35 E1F GAAGCTGGGAGAGCAGAGAA E1R GATGAGGCGAGAGGGACTC 2 E2F TAAAGTGCCTTGCCTCGTCT 60 1X 1,5 35 E2seqF TGCCTTGCCTCGTCTTGTT E2R GAGGAATGTCACCACCATCC 3 E3F TCATCGAGCAGAAATGATGC 55 2X 1,5 35 E3F TCATCGAGCAGAAATGATGC E3R GGCTTAAAATTAATGAAATGCAAAAGA 4 E4F CCAAATATTAAGGTAATGGATATGGA 55 or 60 2X 1,5 35 E4seqR CTGGGTTCCTTCAAAATGGA E4R ACACTTCCTCCTGGGTTCCT 5 E5+6F TTCTGCTGACATGGGGAGAT 60 1X 1,5 35 E5+6F TTCTGCTGACATGGGGAGAT E5+6R GCTCAATGAACCTTTTGTGGA 6 E5+6F TTCTGCTGACATGGGGAGAT 60 1X 1,5 35 E5+6F TTCTGCTGACATGGGGAGAT E5+6R GCTCAATGAACCTTTTGTGGA 7 E7F GCCTAGTTCTACCCCACTAATTC 60 1X 1,5 35 E7seqR CTGATATCTCAATTATCTAAC E7R CCAAGTCAAAGAGGGCATGT 74

8 E8F CTTGGTCAAGGGAGGGATCT 60 1X 1,5 35 E8seqF CCCAGAGACTTTGAATGCTG E8R TGCCTACCACTTACTGAGCAAA 9 E9F TAGGGAGGGCAGAGAGGATT 55 or 60 2X 1,5 35 E9F TAGGGAGGGCAGAGAGGATT E9R GAAATTAAAGCAATTGATGAGCA 10 E10F AAGGCCATTCCATGTATCTCA 60 2X 1,5 35 E10F AAGGCCATTCCATGTATCTCA E10R CAGTCTGTGCAATATAAGGAAACA 11 E11F TCTCTTTCATCTTTCAATGTCAGTTT 55 or 60 2X 1,5 35 E11F TCTCTTTCATCTTTCAATGTCAGTTT E11R GGAAGGCATACCAAGGGAAT 12 E12F TGAATGAAAATGCCAGTGGA 55 or 60 2X 1,5 35 E12F TGAATGAAAATGCCAGTGGA E12R ATTCTCACGGCCTCATTCTG 13 E13+14F ATGTTGGGAATTGACCAGGA 60 1X 1,5 35 E13R CCTGGCTTTCCAAAGATGAG E13R CCTGGCTTTCCAAAGATGAG 14 E13+14F ATGTTGGGAATTGACCAGGA 60 1X 1,5 35 E14F TGCTAAAAGACCCCTGGAGA E13+14R ACATCACCCCCATGATGAAC 15 E15+16F GGAGACACAGCAAGACTCCA 60 1X 1,5 35 E15+16F GGAGACACAGCAAGACTCCA E15R AATGCCAACACTGCATGACT 16 E16F GTTGTGCTCAAGCATCCAGA 60 1X 1,5 35 E16seqF GCTCAAGCATCCAGATTATCTTT E16R TGAATTCTCCTGTAATGGCACA 17 E17+18F CAAGTTATTAAACCCATTAAATTGAGA 60 1X 1,5 35 E17+18F CAAGTTATTAAACCCATTAAATTGAGA E17+18R CAAAAGGCCCTTCTTCATCA 18 E18F GACCTGTGCCTTTCTTTGGA 60 1X 1,5 35 E18seqF CCTGTGCCTTTCTTTGGATT E18R CAAAGTGCTGCGATTACAGG

75

19 E19F GAGAAAGGCCTGTGAGTTGC 55 or 60 2X 1,5 35 E19F GAGAAAGGCCTGTGAGTTGC E19R AGCCTGGCTGATCTGTGTTT 20 E20F CTGATTTGCAACAGCACTGA 60 1X 1,5 35 E20seqF GATTTGCAACAGCACTGATA E20R AGAGATGGGGTTTCATCGTG 21 E21F GGCTCGTTCTTCAGATTTGC 60 1X 1,5 35 E21seqF TTCAGATTTGCCCCATATCC E21R ACGCCTGTCACCTCTAGCTT 22 E22+23F TGATCACATCACGACCGTTT 60 1X 1,5 35 E22+23F TGATCACATCACGACCGTTT E22+23R GCTAGTTTGGTTAACGTCATCTG 23 E23F GAGCCCCTGTGGAGGATTAC 60 1X 1,5 35 E23F GAGCCCCTGTGGAGGATTAC E22+23R GCTAGTTTGGTTAACGTCATCTG 24 E24F AGGCTGGACTTGAAGATTGTAA 55 or 60 2X 1,5 35 E24F AGGCTGGACTTGAAGATTGTAA E24R TCACCAATAGGAACAATTCACC 25 E25+26F CACTCCTGCCTGTGAATGTG 55 or 60 2X 1,5 35 E25+26R ACCCATCTCCCAGTGTCAAA E25+26R ACCCATCTCCCAGTGTCAAA 26 E25+26F CACTCCTGCCTGTGAATGTG 55 or 60 2X 1,5 35 E25+26R ACCCATCTCCCAGTGTCAAA E25+26R ACCCATCTCCCAGTGTCAAA 27 E27+28F AGCATGTGATTTCTGTTCTTCA 6555TD; 60 2 1X 1,5 25+35 2 E27+28F AGCATGTGATTTCTGTTCTTCA E27+28R GGAAGGCAACTGCTGTAGGA 28 E27+28F AGCATGTGATTTCTGTTCTTCA 6555TD; 60 1X 1,5 25+35 E27+28F AGCATGTGATTTCTGTTCTTCA E27+28R GGAAGGCAACTGCTGTAGGA 29 E29+30F CCAAGAGTTCAAGGCCAGTC 60 1X 1,5 35 E29R AAATTTAGCACAAACAACTCACTTTT E29R AAATTTAGCACAAACAACTCACTTTT

76

30 E30F TGATCTTGTGCTTTTGACAGG 60 1X 1,5 35 E30seqR GAAGCCGGATTCGATGACTA E30R TGACACAAGTTATTTTGATTTGTTGA 31 E31+32F CGTAAATGACGTGGGCTAGA 60 1X 1,5 35 E31R CCCCAAAATCGGTGCTATTA E31R CCCCAAAATCGGTGCTATTA 32 E32F GGGCAACAAGAGCAAAACTC 60 1X 1,5 35 E32ampF GGGCAACAAGAGCAAAACTC E31+32R CACTGTGCAGGCAGGAGTAG 33 E33+34F TTCCTCCTCCTGGGTTCTTT 60 1X 1,5 35 E33R CTTAAGGACTTGCAGGACCAC E33R CTTAAGGACTTGCAGGACCAC 34 E34F GGTGAATTGGATTTGCAGGT 60 1X 1,5 35 E34seqF GGTGAATTGGATTTGCAGGT E34R AGGGCCACCATCTTATGTGA 35 E35F TGTGATATTCGTGACTGTTAAATTCC 60 1X 1,5 35 E35F TGTGATATTCGTGACTGTTAAATTCC E35R GTCTGCGTCCCACCTACG E35R GTCTGCGTCCCACCTACG 1The 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 (T m) of the primertemplate duplex, and, therefore, the annealing temperature. In certain cases, increasing the buffer concentration to 2X reduces or eliminates background noise caused by nonspecific amplification.

2Touchdown PCR – aimed at reducing nonspecific 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 out compete nonspecific sequences . In this experiment, the annealing temperature was lowered from 65 to 55˚C over the first 25 cycles after which, a regular 35cycle 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.

Amplicon Cut Cuts Exon Primers Enzyme size size 1 (wt/m) 2 GGGAGACACAGCAAGACTCC 15 237 211 AvaII / Sau96I wt TCTAAAAGGAATTCCTTCAGGAG CCAAAAATTTGAGGCACACC 18 220 188 RsaI wt CAAAAGGCCCTTCTTCATCA TGATTTTAAATGTATATTTTCAGG 28 226 172 RsaI / ScaI m CACCAGGCAACGTGTAATGT GCAACATCGTGAGAAACCAT 29 250 148 Sai3AI / MboI m CCAACCATCATCTGCTGAAA 1Size of PCR fragment after restrictionenzyme digestion. 2“wt” wild type: restriction enzyme cuts the wildtype 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. RajcanSeparovic 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, RP11294K6 (6514 to 6537 kb) and RP11143E20 (7634 to 7805 kb).

Realtime 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 8Mb

RP11366M24 RP11294K6 RP11143E20 RP11657D19 (Balanced) (Deleted) (Deleted) (Balanced)

N RPS2HDH STS PN LGN PL A 4 7 D 4X AP171A

Deleted in Subject 1

Figure 3.1 Refinement of the breakpoints of the deletions on chromosome Xp22 in Subject 1

using realtime 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. ArrayCGH 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.

Position on Gene symbol Gene name chromosome (kb)

RPS27AP17 ribosomal protein S27a pseudogene 17 6917 6918

HDHD1A haloacid dehalogenaselike hydrolase domain containing 1A 6977 7076

STS steroid sulfatase (microsomal), isozyme S 7147 7283

VCX variable charge, Xlinked 7770 7772

PNPLA4 patatinlike 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 nonpolymorphic 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 ASDrelated 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 HardyWeinberg equilibrium in cases and controls. There was an apparent increased transmission of the rs1316392C 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 overtransmitted 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 Familybased association testing of single markers within the NLGN4X and VCX genes with autism.

Position on All SPX 2 MPX 3 Gene Marker Allele chromosome (bp) fam# 1 Z P fam# Z P fam# Z P T 0,118 -0,012 -0,298 NLGN4X A (rs1882411) 5 982 382 301 0,91 142 0,99 91 0,77 C 0,118 0,012 0,298 T 0,121 1,163 0,243 B (rs5961933) 6 045 657 250 0,9 124 0,24 73 0,81 C 0,121 -1,163 -0,243 T 1,22 0,512 -1,007 C (rs10856356) 6 058 854 303 0,22 142 0,61 97 0,31 G 1,22 -0,512 1,007 T 1,08 -0,163 1,536 D (rs1997481) 6 062 796 299 0,28 144 0,87 91 0,12 C 1,08 0,163 -1,536 C 1,855 -0,583 2,806 E (rs1316392) 6 063 605 224 0,06 104 0,56 71 0,0050 4 T 1,855 0,583 -2,806 G 1,302 0,725 0,792 VCX F (rs6530117) 7 766 313 264 0,19 123 0,47 85 0,43 C 1,302 -0,725 -0,792 T 0,763 0,566 0,651 G (rs845125) 7 786 598 306 0,45 152 0,57 95 0,51 A 0,763 -0,566 -0,651 G 0,134 -0,258 0,676 H (rs4118155) 7 788 006 272 0,89 133 0,80 87 0,50 A 0,134 0,258 -0,676

1fam#: number of informative families. 2SPX: simplex families – families with only one affected individual. 3MPX: multiplex families – two or more affected individuals. 1 4Not significant after FDR correction: .0 005 > × .0 05 ⇔ 0.0021 24

85 Table 3.5 Familybased haplotype association for the markers in the NLGN4X gene in families with ASD.

Haplotype All Spx 2 Mpx 3 A B C D E fam# 1 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

1fam#: number of informative families. 2SPX: simplex families – families with only one affected individual 3MPX: multiplex families – two or more affected individuals.

86 Table 3.6 Familybased haplotype association for the markers in the VCX gene in families with ASD.

Haplotype All Spx 2 Mpx 3 F G H fam# 1 Z P fam# Z P fam# Z P

C A A 272 1,39 0,16 125 0,55 0,58 83 1,15 0,25 C T G 194 0,54 0,59 101 0,79 0,43 55 0,57 0,57 G A A 149 2,03 0,042 4 62 1,29 0,20 50 1,11 0,27 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. 2SPX: simplex families – families with only one affected individual 3MPX: multiplex families – two or more affected individuals. 1 4Not significant after FDR correction: .0 042 > × .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 2p1516.1: RP1181L7 (57 007 to 57 160 kb),

RP1190D1 (59 125 to 59 326 kb), RP1181L13 (60 159 to 60 327 kb), RP1179K21 (61 071 to

61 072 kb) and RP11355B11 (61 513 to 61 673 kb).

The same DNA was also deleted in Subject 2 as well as DNA representing two telomeric

clones: RP11494H5 (55 796 to 55 996 kb) and RP11482H16 (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 Realtime 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 nonoverlapping region.

88

SubjectPatient 1 1

SubjectPatient 2 2

Chabchoub et al.

De Leeuw et al. Figure 3.2 Refinement of the breakpoints of the deletions in Subjects 1 and 2 using realtime 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. Bicolored clones were deleted only in Subject 1. The genes from VRK2 to OTX1 are deleted in both patients. ArrayCGH 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 Probe chromosome (kb) Subject 1 Subject 2

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

2pProximal9 63 322 bal del

2pProximal9-A 63 338 nt del

2pProximal9-B 63 364 nt del

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.

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Table 3.8 Distal 2p16 breakpoint refinement in Subject 1.

Position on chromosome Probe Deletion (kb) 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.

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Table 3.9 Distal 2p16 breakpoint refinement in Subject 2.

Probe 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

“del”: deletion (hemizygocity) detected; “bal” marker is balanced.

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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 non polymorphic 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, OTX1 was 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 HardyWeinberg 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 Familybased association testing of single markers within the OTX1 gene with autism. Position on All Spx 2 Mpx 3 Marker Allele chromosome (bp) fam# 1 Z P fam# Z P fam# Z P C 0,138 0,521 0,109 A (rs6739804) 63 123 108 437 0,890 179 0,600 121 0,910 T 0,138 0,521 0,109 G 1,067 1,125 0,000 B (rs2075375) 63 134 194 464 0,290 189 0,260 130 1,000 A 1,067 1,125 0,000 A 0,438 0,070 113 0,000 C (rs11125946) 63 151 654 392 0,660 151 0,940 1,000 G 0,438 0,070 0,000

1fam#: number of informative families.

2SPX: simplex families – families with only one affected individual.

3 MPX: multiplex families – two or more affected individuals.

95 Table 3.11 Haplotype familybased association testing of markers within the OTX1 gene with autism.

Haplotype All Spx 2 Mpx 3 A B C fam# 1 Z P fam# Z P fam# Z P 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 0,108 0,910

1fam#: number of informative families.

2SPX: simplex families – families with only one affected individual.

3MPX: multiplex families – two or more affected individuals.

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Table 3.12 Estimated frequencies of haplotypes in the OTX1 gene in affected individuals and controls.

Haplotype Cases 1 Controls 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 Rare 2 17 0.013 3 0.010 P = 0.947 1One affected individual was randomly selected in each multiplex family; all affected individuals from simplex families were included.

2All haplotypes with less than 5 occurrences in either cases or controls were pooled in the rare haplotype category.

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LINEs (short and long interspersed elements, respectively), long terminal repeats (LTRs) of retroviruslike 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

LINE1 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, CTB51J22 and B270D13 spanning the WBS region (Figure 3.3). The duplication was confirmed with realtime 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 nonpolymorphic 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 lowcopy repeats, blocks

Bc and B m, 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 real time sqPCR. No additional cases of dup/del(7)(q11.23) were identified.

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ABC DEFG HIJ

STX1A CYLN2 GTF2i

Cc Ac Bc Cm Bm Am Bt At Ct RP11-35P20 B315H11 CTB-51J22 B270D13 RP11-89A20

Common WBS deletion Malenfant et al. Edelmann et al. ASD Depienne et al. 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. ArrayCGH experiments were performed by Dr Ying Qiao and

Chansonette Harvard.

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Table 3.13 Proximal 7q11 breakpoint refinement in Subject 3. Probe Position on chromosome (kb) Duplication 7qProximalA 71 674 bal 7qProximalB 71 724 bal 7qProximalC 71 809 bal 7qProximalD 71 872 bal 7qProximalE 71 935 bal 7qProximalF 72 002 bal 7qProximalG 72 048 bal 7qProximalH 72 102 bal 7qProximalI 72 187 bal 7qProximalJ 72 253 bal 7qProximalK 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 7qProximalL 72 385 dup 7qProximalM 72 431 dup 7qProximalN 72 512 dup 7qProximalO 72 575 dup 7qProximalP 72 634 dup 7qProximalQ 72 690 dup

Markers used for fine refinement are italicized and were tested following larger scale refinement. “dup”: duplication detected; “bal” probe is balanced.

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Table 3.14 Distal 7q11 breakpoint refinement in Subject 3..

Probe Position on chromosome (kb) Number of copies

7qDistalA 73 355 dup 7qDistalB 73 380 dup 7qDistalC 73 409 dup 7qDistalD 73 433 dup 7qDistalE 73 465 dup 7qDistalF 73 487 dup 7qDistalG 73 515 dup 7qDistalH 73 540 dup 7qDistalI 73 586 dup 7qDistalJ 73 642 dup 7qDistalK 73 702 dup 7qDistalL 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 7qDistalM 73 806 bal 7qDistalN 73 861 bal 7qDistalO 73 921 bal 7qDistalP 73 975 bal 7qDistalQ 74 025 bal 7qDistalR 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.

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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 HardyWeinberg equilibrium in both family cohorts. Results from singlemarker familybased association tests are summarized in Table 3.15. There appeared to be an increased transmission of the rs1569061T 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 (rs4717907A: P = 0.039; rs13227433G: 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 (rs4717907A: P = 0.0010; rs13227433G: 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 over

transmission of any haplotype in either STX1A or CYLN2 in the combined or simplex groups but

there appeared to be a modest overtransmission 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 overtransmission in the multiplex families 102 Table 3.15 Singlemarker familybased association testing for the markers in the STX1A, CYLN2 and GTF2i genes in families with ASD. Position on All Spx 2 Mpx 3 Gene Marker Allele chromosome (bp) fam# 1 Z P fam# Z P fam# Z P4 C 2,00 0,58 -2,09 STX1A A(rs1569061) 72 752 417 172 0,045 96 0,56 76 0,037 T 2,00 0,58 2,09 C 1,04 1,07 -0,49 B(rs4363087) 72 756 132 440 0,30 242 0,29 198 0,62 T 1,04 1,07 0,49 A 1,55 1,49 -0,79 C(rs3793243) 72 759 283 447 0,12 249 0,14 198 0,43 G 1,55 1,49 0,79 A 0,00 0,12 -0,10 CYLN2 D(rs229890) 73 355 107 382 1,00 215 0,9 167 0,92 G 0,00 0,12 0,10 C 1,00 0,83 0,61 E(rs6964387) 73 365 179 69 0,32 39 0,4 30 0,54 T 1,00 0,83 -0,61 C 0,15 0,12 0,30 F(rs480487) 73 393 034 388 0,88 217 0,9 171 0,77 T 0,15 0,12 -0,30 A 0,34 0,04 0,39 G(rs523434) 73 398 640 371 0,74 204 0,97 167 0,69 G 0,34 0,04 -0,39 A 2,07 0,88 3,30 GTF2i H(rs4717907) 73 724 429 358 0,039 196 0,38 162 0,0010 G 2,07 0,88 -3,30 G 2,04 0,49 2,95 I(rs13227433) 73 732 657 364 0,041 196 0,62 168 0,0032 T 2,04 0,49 -2,95 A 0,29 1,02 1,16 J(rs2718270) 73 734 295 192 0,78 109 0,31 83 0,25 G 0,29 1,02 -1,16 1fam#: number of informative families. 2SPX: simplex families – families with only one affected individual. 3MPX: multiplex families – two or more affected individuals. 4Bold: P <0.05; Bold underlined: significant after FDR correction. 103

Table 3.16 Familybased haplotype association for the markers in the STX1A, CYLN2 and GTF2i

genes in families with ASD.

STX1A CYLN2 GTF2i Group afreq 1 fam# 2 S3 E(S) 4 Var(S) 5 Z P6 A B C D E F G H I J T T 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

G T C G 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

A G A 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

A G 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

1afreq: haplotype frequency in all families 2 fam#: number of informative families for the test. 3 S: number of occurrences of the haplotype in the affected offspring in informative families 4E(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.

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(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 overtransmission was observed in the simplex families ( P = 0.615). When SNP rs2718270, which was not shown to have an association in

singlemarker 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 resequenced in 7 individuals

with autism to identify any ASDrelated 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

level (Table 3.18). RFLPs were used to confirm the status of the 5 potential mutations and all 5

were found to be falsepositives (Figure 3.5). Therefore, no sequence variants were found in the

exons of GTF2i gene.

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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/A 2 44 Yes Yes Yes N/A 2250 Yes Yes Yes Yes 3 2253 Yes Yes Yes Yes 4 40084 No Yes Yes N/A 40816 Yes Yes No 1 N/A 41918 Yes Yes Yes N/A 1Two of three affected individuals were homozygous; the third individual is a paternal halfsibbling. 2N/A: all sibblings were probands. 3One unaffected sister with no copy of the haplotype. 4One unaffected brother heterozygous for the haplotype.

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80 85 90 a) A A K R A C TT CC T G A

70 75 80 b) G A TTC GGT R C T GG A A

35 30 25 c) G C AAG TTS T A T AA C

70 65 60 55 d) T A AAAG G RTTTTTC A CCA 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 400842943 (b) apparent heterozygous substitution on exon 18 in proband 419187688 (c) exon 28 in proband 2278 (d) exon 29 in proband 44134.

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Table 3.18 Potential sequence variations within the GTF2i gene detected by sequencing.

Position Effect on amino Exon Case ID Expected Observed in exon (bp) acid sequence 15 400842943 81 G K (G/T) Gly:STOP 15 400842943 82 G R (A/G) Gly:Glu 18 419187688 73 A R (A/G) Tyr:Cys 28 2278 31 C S (G/C) Thr:Ser 29 44134 65 C Y (C/T) no change

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 pre optimized to ensure complete digestion (a) Proband 400842943 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. Nondigested DNA (“nd”) served as the positive control. (b) Proband 419187688 is wild type for base pair 73 of exon 18 – cleavage by RsaI

(GTAC) is only possible with the wild type sequence. Nondigested DNA (“nd”) served as the positive control. (c) Wildtype sequence on exon 28 in proband 2278 – RsaI (GTAC) and ScaI

(AGTACT) digest the amplicon in presence of the mutation. Nondigested DNA serves as negative control. (d) Wild type exon 29 in proband 44134 – Sau3AI (GATC) and MboI (GATC) cleavage occurs on mutated sequence. Nondigested DNA serves as negative control.

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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 ArrayCGH and FISH are widely used for the detection of DNA copy number variations.

FISH is timeconsuming and atypical abnormalities may not be detected if the probe used does not map precisely to the entire region. ArrayCGH remains too expensive to screen large numbers of patients for the moment. In contrast, realtime sqPCR is a wellestablished method for detecting copynumber 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 costefficient than most, including FISH and arrayCGH.

Chromosome abnormalities can be screened in a highthroughput experiment with a set of low cost, conventional PCR primers, using less than 10 ng of genomic DNA per sample. The SYBR

Green dye detection method used herein is more costeffective than TaqMan™ assays, which requires the synthesis of fluorescencelabeled 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 arrayCGH, Multiplex Amplifiable Probe Hybridisation (MAPH) and

Multiplex ligationdependent 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 retested 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 diseasecausing 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 autismsusceptibility 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 casecontrol 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 casecontrol 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).

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Collecting family data for TDT generally requires more resources in terms of time and money (Laird & Lange, 2006), but familybased 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 nonrandom 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 (casecontrol) 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 Pvalues. 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 familywise 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 Pvalues. 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.

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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: PDDNOS 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 nonMendelian inheritance factors such as incomplete penetrance, genegene and geneenvironment interactions may also hinder the search for ASD genes.

114

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 ASDcausing 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 arraybased techniques allow the genome

wide 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.

115

4.3 Copy-number variations and autism spectrum disorders With the advent of genomewide 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 ASDrelated genes, and to identify these autismsusceptibility genes. The overall hypothesis was that deletions and duplications found in individuals with ASD would signal the locations of ASDrelated 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

116 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 CNVassociated 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 1520 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 genegene interactions could modify biological functions and need to be

considered. A singlecopy 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 arrayCGH are ultimately not confirmed by sqPCR. This rate is similar to those reported by others (BarShira 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 (6.57.9 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,

117 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 largescale 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, Xlinked 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 X

linked chondrodysplasia punctata (arylsulfatase E ; ARSE ), short stature (short stature homeobox;

SHOX ), Xlinked ichthyosis (Steroid sulfatase; STS ), Kallmann syndrome ( KAL ), and ocular

albinism type 1 (G proteincoupled 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 maletofemale 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

118 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 Xlinked disorders, support this hypothesis.

As is well known, females have two functional copies of each gene on the Xchromosome,

while males may have one or two, depending on whether there is a Ylinked 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 Yhomologues, with which they share a high degree of sequence identity, and which are transcriptionally active. Thus, I hypothesize that in the father, these Y homologues are sufficient to compensate for the loss of their Xlinked counterpart but, in the daughter, no such compensation is possible in the cells where Xinactivation of non Xp22 deleted chromosome occurred. Indeed, deletions on the Xchromosome are commonly paternal in origin.

For example, there is a loss of the paternal X chromsome in 20/29 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

119 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 rs1316392C 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 tagSNPs tested in NLGN4X or VCX .

Although tagSNPs 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 multigeneration 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

120 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) resequenced 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 Xlinked 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 chromatin associated proteins (Lahn & Page, 2000). The Nterminal domain, which is conserved amongst all

VCX/Y members, contains several basic residues and is therefore positively charged. In contrast, their Cterminal 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 114 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, 121

1 and 1 repeats, respectively. Van Esch et al. (2005) suggested a dosagedependant 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

chromatinassociated 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 (nonsynonymous) mutations have been reported in individuals with autism, mental retardation or PDDNOS (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; YlisaukkoOja et al. , 2005; Wermter et al. , 2008). YlisaukkoOja

(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 neuroliginneurexin pathway have also been identified in ASD families 122

(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 nonrandomly mating populations.

4.4.2 Del(2)(p15-p16.1) Characterization of two overlapping 2p15p16.1 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 LINE1 repeats.

LINE1 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,

LINE1 retrotransposons have been implicated in genomic rearrangements. Korbel et al. (2007)

reported that LINE1 elements were significantly enriched at the breakpoints of copynumber

variants when compared to the genomic background. LINE1 elements present in both 123

Table 4.1 Genes uncovered by the deletions in Subjects 1 and 2 and their position on chromosome 2.

Position on chromosome (kb) Gene symbol Gene name Start End CCDC104 coiledcoil domain containing 104 55 600 55 626 SMEK2 SMEK homolog 2, suppressor of mek1 55 629 55 698 (Dictyostelium) PNPT1 polyribonucleotide nucleotidyltransferase 1 55 716 55 774 EFEMP1 EGFcontaining fibulinlike extracellular matrix 55 947 56 004 protein 1 MIRN217 microRNA 217 56 064 56 064 MIRN216A microRNA 216a 56 070 56 070 MIRN216B microRNA 216b 56 081 56 081 LOC100129434 hypothetical protein LOC100129434 56 254 56 266 CCDC85A coiledcoil domain containing 85A 56 265 56 467 LOC647016 similar to Eukaryotic translation initiation factor 2 57 129 57 134 subunit 2 (Eukaryotic translation initiation factor 2 beta subunit) (eIF2beta) LOC100131953 hypothetical LOC100131953 57 846 57 847

VRK2 vaccinia related kinase 2 58 127 58 241 FANCL Fanconi anemia, complementation group L 58 240 58 322 LOC339799 hCG15200 58 332 58 338 LOC644456 similar to RED protein 58 541 58 543 LOC730134 hypothetical protein LOC730134 59 319 59 331 LOC647038 hypothetical LOC647038 59 376 59 469 BCL11A Bcell CLL/ 11A (zinc finger protein) 60 532 60 634 LOC442017 interferon induced transmembrane protein 60 763 60 764 pseudogene LOC130865 similar to 60S ribosomal protein L26 (Silica 60 792 60 793 induced gene 20 protein) (SIG20) ATP1B3P1 ATPase, Na+/K+ transporting, beta 3 pseudogene 60 815 60 817 PAPOLG poly(A) polymerase gamma 60 837 60 880 REL vrel reticuloendotheliosis viral oncogene homolog 60 962 61 004 (avian) LOC344423 similar to ribosomal protein S12 61 018 61 020 PUS10 pseudouridylate synthase 10 61 023 61 099 PEX13 peroxisome biogenesis factor 13 61 098 61 130 KIAA1841 KIAA1841 61 147 61 219 LOC339804 hypothetical gene supported by AK075484; 61 226 61 245 BC014578 AHSA2 AHA1, activator of heat shock 90kDa protein 61 258 61 268 ATPase homolog 2 (yeast) USP34 ubiquitin specific peptidase 34 61 268 61 551 124

LOC100130280 hypothetical LOC100130280 61 330 61 331

SNORA70B small nucleolar RNA, H/ACA box 70B 61 498 61 498 XPO1 exportin 1 (CRM1 homolog, yeast) 61 559 61 619 LOC100132037 similar to mCG7602 61 670 61 670

LOC647077 similar to 60S ribosomal protein L14 (CAGISL 7) 61 791 61 792 FLJ13305 hypothetical protein FLJ13305 61 907 61 935 LOC100127901 hypothetical LOC100127901 61 939 61 947

CCT4 chaperonin containing TCP1, subunit 4 (delta) 61 949 61 969 COMMD1 copper metabolism (Murr1) domain containing 1 61 986 62 217 LOC729209 similar to 40S ribosomal protein SA (p40) (34/67 62 222 62 228 kDa laminin receptor) (Colon carcinoma laminin binding protein) (NEM/1CHD4) (Multidrug resistanceassociated protein MGr1Ag) LOC100131923 hypothetical protein LOC100131923 62 238 62 292

B3GNT2 UDPGlcNAc:betaGal beta1,3N 62 277 62 305 acetylglucosaminyltransferase 2 LOC100130512 hypothetical LOC100130512 62 430 62 446

TMEM17 transmembrane protein 17 62 581 62 587 LOC100129141 similar to ribosomal protein L21 62 613 62 614

LOC100129162 similar to hCG1644578 62 634 62 642

EHBP1 EH domain binding protein 1 62 787 63 127 LOC100132215 hypothetical protein LOC100132215 63 126 63 129

OTX1 orthodenticle homeobox 1 63 131 63 138 LOC51057 hypothetical protein LOC51057 63 202 63 669 The genes deleted in both patients are indicated in bold. Others were only deleted in Subject 2.

125 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 2p1516.1 region have been reported in control subjects, including a recurrent 2.9 Mb deletion/duplication in healthy individuals (58.261.1 Mb; http://projects.tcag.ca/variation/) encompassing many genes such as

PEX13 , vrel reticuloendotheliosis viral oncogene homolog (avian) ( REL ), and FANCL . A subject with a de novo duplication of band 2p16.1 (clones RP11201J10 to RP1144OP5; ~57.1

Mb60.7 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 (RajcanSeparovic et al. , 2007) and did not have an ASD. In fact, recurring de novo microdeletions of various sizes have been detected within the

2p1516.1 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)(p1516.1), 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).

126

The above findings strongly suggest that the 2p1516.1 region is unstable and that a large contiguous deletion of critical genes leads to a recognizable syndrome. To determine the frequency of 2p15p16.1 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 2p1516.1 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 2p1516.1 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 RajcanSeparovic 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

127 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 2pdeleted 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, family based 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 2p1516.1 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 2p1516.1 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 2p1516.1 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 2p1516.1 deletions in

128 other screening studies. The findings in the present study suggest that the newly described 2p15

16.1 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 DSMIV diagnostic criterion for autism. The breakpoints of this 7q11.23 duplication were localized within the low copy 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 129

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 nonspecific 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). Single

marker 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 overtransmission 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 overtransmission of one allele (rs1569061T) in

the STX1A gene was initially observed in multiplex but not in simplex families in singlemarker

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%).

130

Of the three candidate genes examined in 7q11.23, GTF2i proved to be the most

interesting. There was strong overtransmission of the common alleles for two of the three SNPs

tested in multiplex families (rs4717907A and rs13227433G: 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 singlemarker 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, overtransmission 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 (rs4717907rs13227433rs2718270) may be associated with mutations or other variants that contribute to some elements of the ASD phenotype in a subset 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 selfidentified and recruited online by ASDCARC

(www.AutismResearch.ca). This strengthens my conviction that this association is real.

131

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 nonautismassociated WBS, its duplication or altered expression (in some cases due to at least two copies of a mutant form of this gene, resulting from nonhomologous 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 TFIII 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 nonredundant

(Cheriyath & Roy, 2000). The γisoform is predominantly expressed in neuronal cells (Perez

Jurado et al. , 1998). Several pathways are regulated by the different isoforms of the TFIII transcription factor and disruption of one or more of these pathways could lead to autism or an autismrelated endophenotype. For example, Caraveo et al . (2006) showed that TFIII reduces the

Ca 2+ channeling activity of the transient receptor potential channel 3 molecule (encoded by the

TRPC3 gene). It is known that mutations of voltage activated Ltype Ca 2+ 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 brainderived 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

132 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 resequencing 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 ASD associated 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 overtransmission 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 halfsibling. 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 ASDassociated symptoms.

4.5 Conclusions and recommendations The development of arraybased technologies has allowed the highresolution 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 . 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.

Timeconsuming expressionlevel 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 2p1516.1 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 ASDassociated 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 autismsusceptibility 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). Highresolution 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 generich 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 firstdegree 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 autismassociated 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 LCRmediated 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 ASDCARC 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 .

Multipleincidence 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 ASDcausing 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 ASDCARC 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 timeconsuming 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 microarraysbased experiments is rapidly decreasing. Wholegenome association studies, which were cost and labourprohibitive 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). Largescale 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 multidisciplinary cooperation, combined with diligence, will continue to be required to truly understand and solve the puzzle of autism.

138

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