A Genetic Association Study of Glutamate Transporter ......1.1 Introduction to Tourette syndrome The...
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A Genetic Association Study of Glutamate Transporter Genes SLC1A1 and SLC1A3 in Tourette syndrome and Attention Deficit/Hyperactivity Disorder by Alexandra Kentebe A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto © Copyright by Alexandra Kentebe 2014
Transcript of A Genetic Association Study of Glutamate Transporter ......1.1 Introduction to Tourette syndrome The...
A Genetic Association Study of Glutamate Transporter
Genes SLC1A1 and SLC1A3 in Tourette syndrome and
Attention Deficit/Hyperactivity Disorder
by
Alexandra Kentebe
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Institute of Medical Science
University of Toronto
© Copyright by Alexandra Kentebe 2014
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A Genetic Association Study of Glutamate Transporter Genes SLC1A1 and SLC1A3 in Tourette syndrome and Attention
Deficit/Hyperactivity Disorder
Alexandra Kentebe
Master of Science
Institute of Medical Science
University of Toronto
2014
Abstract Tourette syndrome (TS) is a heterogeneous disorder that may share etiological factors with
attention-deficit/ hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD).
Genetic factors play a large role in the pathogenesis of these disorders and disruption of the
glutamatergic system has been implicated through multiple lines of study. On this basis, the
current investigation evaluates the role of two glutamate transporter genes: SLC1A1 and
SLC1A3. SLC1A1 was selected for study based on its function and involvement in OCD, and
was tested for association with TS and ADHD, using two independent samples. SLC1A3 was
selected for study based on function and possible involvement in ADHD, and was tested for
association using our TS sample. None of the genotyped markers remained significant
following corrections for multiple testing. Considering that we observed several trends for
association, studies using larger samples are required to determine whether these genes are
associated with TS or ADHD.
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Acknowledgments
I would like to thank my supervisor, Dr. Cathy L. Barr for giving me the opportunity
to learn from her and work in her lab. Without her guidance and patience, this thesis would
not have been possible. I am also grateful for the members of the Barr lab: Kathryn Tzimika,
Karen Wigg, Yu Feng, Lissette Gomez, Ginger Misener, Christopher Tran, Sabah Husain
and Kevin Zhang, for their support and advice during my program. To my Program Advisory
Committee: Dr. Paul Sandor, Dr. Jo Knight and Dr. Margaret Richter, thank you for your
time, your advice and your encouragement. I would also like to thank Dr. Cindi Morshead
for her support throughout my program.
Thank you to the Institute of Medical Science, The Tourette Syndrome Association of
America, the Ontario Mental Health Foundation, The Tourette Syndrome Foundation of
Canada, and the Canadian Institute of Health Research (CIHR) for financially supporting this
project.
Finally, I would like to thank my friends and family for their support during my
program. Thank you to my husband, Ryan, for getting me through the tough days, for being
understanding during the busy days and for your continuous encouragement.
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Table of Contents Abstract(.............................................................................................................................................(ii!
Acknowledgments(........................................................................................................................(iii!
Table(of(Contents(..........................................................................................................................(iv!
List(of(Tables(................................................................................................................................(viii!
List(of(Figures(.................................................................................................................................(ix!
List(of(Abbreviations(.....................................................................................................................(x!
! Introduction(................................................................................................................(1!Chapter!1
1.1! Introduction(to(Tourette(syndrome(.......................................................................................(1!
1.2! Evidence(of(a(genetic(basis(of(TS(.............................................................................................(2!
1.3! Model(of(inheritance(of(TS(.........................................................................................................(3!
1.4! Identification(of(genetic(susceptibility(factors(...................................................................(4!
1.4.1! Linkage!studies!.........................................................................................................................................!4!
1.4.2! Association!studies!.................................................................................................................................!5!
1.4.3! Linkage!Disequilibrium!........................................................................................................................!8!
1.5! The(Behavioral(Spectrum(of(TS(.............................................................................................(10!
1.5.1! Chronic!Multiple!Tics!..........................................................................................................................!11!
1.5.2! TS!comorbidities!and!potential!TS!subgroups!.........................................................................!12!
1.5.3! Comorbid!TS!with!OCD!......................................................................................................................!12!
1.5.4! Evidence!of!genetic!overlap!between!TS!and!OCD!.................................................................!14!
1.5.5! Comorbid!TS!and!ADHD!....................................................................................................................!15!
1.5.6! Relevance!.................................................................................................................................................!17!
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1.6! The(Glutamatergic(System(.......................................................................................................(17!
1.7! The(Glutamatergic(Hypothesis(...............................................................................................(19!
1.7.1! Evidence!implicating!the!glutamatergic!system!in!TS!..........................................................!20!
1.7.2! Evidence!implicating!the!glutamatergic!system!in!ADHD!..................................................!21!
1.7.3! Evidence!implicating!the!glutamatergic!system!in!OCD!......................................................!21!
1.8! Genetic(evidence(of(overlap(between(TS,(ADHD(and(OCD(............................................(22!
1.8.1! Common!susceptibility!loci!of!TS,!ADHD!and!OCD!................................................................!22!
1.9! Genetic(studies(in(candidate(region(9p(...............................................................................(23!
1.9.1! Linkage!studies!implicating!9p!in!OCD!.......................................................................................!23!
1.9.2! Case!reports!of!9p!chromosomal!abnormalities!in!TS!.........................................................!23!
1.9.3! Candidate!gene!studies!at!9p24!.....................................................................................................!24!
1.10! SLC1A1(..........................................................................................................................................(24!
1.10.1! SLC1A1!genetic!studies!in!OCD!....................................................................................................!24!
1.10.2! Rationale!for!investigating!SLC1A1!in!TS!and!ADHD!.........................................................!27!
1.11! Genetic(studies(in(candidate(region(5p13(.......................................................................(27!
1.11.1! Linkage!studies!implicating!5p13!in!ADHD!............................................................................!27!
1.11.2! Linkage!studies!implicating!5p13!in!TS!...................................................................................!28!
1.12! SLC1A3(..........................................................................................................................................(29!
1.12.1! SLC1A3!studies!of!ADHD!................................................................................................................!29!
1.12.2! SLC1A3!studies!of!TS!........................................................................................................................!30!
1.12.3! Rationale!for!investigating!SLC1A3!in!TS!and!ADHD!.........................................................!31!
1.13! Chromatin(Signatures(and(Gene(Expression(..................................................................(32!
1.14! Research(Aims(&(Hypotheses(..............................................................................................(38!
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! Materials(and(Methods(........................................................................................(39!Chapter!2
2.1! The(TS(familyUbased(sample(...................................................................................................(39!
2.1.1! Recruitment!and!exclusion!criteria!..............................................................................................!39!
2.1.2! Sample!Composition!...........................................................................................................................!39!
2.1.3! Diagnostic!Assessment!.......................................................................................................................!40!
2.2! The(ADHD(familyUbased(sample(............................................................................................(40!
2.2.1! Recruitment!and!exclusion!criteria!..............................................................................................!41!
2.2.2! Sample!Composition!...........................................................................................................................!41!
2.2.3! Diagnostic!&!Behavioural!Assessment!........................................................................................!41!
2.3! Isolation(&(Extraction(of(DNA(.................................................................................................(42!
2.4! SingleUNucleotide(Polymorphism(Selection(......................................................................(42!
2.4.1! SLC1A1!......................................................................................................................................................!42!
2.4.2! SLC1A3!......................................................................................................................................................!42!
2.5! SingleUNucleotide(Polymorphism(Genotyping(..................................................................(43!
2.6! Statistical(Analysis(.....................................................................................................................(44!
2.6.1! Association!Analyses!...........................................................................................................................!44!
2.6.2! Visualization!and!Interpretation!of!LD!blocks!.........................................................................!46!
2.6.3! Quality!Control!......................................................................................................................................!47!
2.6.4! Power!.........................................................................................................................................................!47!
2.6.5! Correction!for!Multiple!Testing!......................................................................................................!50!
! A(familyUbased(association(study(of(the(putative(ObsessiveUChapter!3
Compulsive(Disorder(gene,(SLC1A1, with(Tourette(Syndrome(and(AttentionU
Deficit/(Hyperactivity(Disorder(..............................................................................................(52!
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3.1 ! Introduction(.................................................................................................................................(52!
3.2! SLC1A1'Results(.............................................................................................................................(58!
3.3! Tables(&(Figures(.........................................................................................................................(60!
! A(familyUbased(association(study(of(a(putative(AttentionUChapter!4
Deficit/Hyperactivity(Disorder(gene,(SLC1A3,(with(Tourette(syndrome(.................(64!
4.1! Introduction(.................................................................................................................................(64!
4.2! SLC1A3'Results(.............................................................................................................................(69!
4.3! Tables(&(Figures(.........................................................................................................................(72!
! Discussion(&(Future(Directions(........................................................................(76!Chapter!5
5.1! Discussion(.....................................................................................................................................(76!
5.1.1! SLC1A1!......................................................................................................................................................!76!
5.1.2! SLC1A3!......................................................................................................................................................!79!
5.1.3! Global!Discussion!of!SLC1A1!and!SLC1A3!.................................................................................!80!
5.2! Global(Limitations(......................................................................................................................(82!
5.3! Future(Direction(.........................................................................................................................(84!
5.3.1! SLC1A1!......................................................................................................................................................!84!
5.3.2! SLC1A3!......................................................................................................................................................!86!
5.3.3! Future!Candidate!Genes!for!Study!................................................................................................!88!
5.4! Conclusion(.....................................................................................................................................(88!
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List of Tables Table 1–1. DSM-V Overview of TS, OCD & ADHD ........................................................... 36!
Table 1–2. Summary of Linkage Studies of TS ..................................................................... 37!
Table 3–1. SLC1A1 Single SNP analysis for TS sample ........................................................ 60!
Table 3–2. SLC1A1 Haplotype analysis of Block 3 for TS sample ........................................ 61!
Table 3–3. SLC1A1 Single SNP analysis for ADHD sample ................................................. 62!
Table 3–4. SLC1A1 Haplotype analysis for ADHD sample ................................................... 62!
Table 4–1. Single-marker TDT analysis for 10 genotyped SLC1A3 SNPs in TS sample ...... 72!
Table 4–2. SLC1A3 Haplotype analysis of rs2562571/rs4869675 for TS sample ................. 72!
Table 4–3. SLC1A3 Haplotype analysis of rs2269272/rs1529461 for TS sample ................. 73!
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List of Figures Figure 3-1. LD plot of 17 genotyped SLC1A1 SNPs .............................................................. 63!
Figure 3-2. Relative positions of 17 genotyped SLC1A1 markers ......................................... 63!
Figure 4-1. LD plot of 10 genotyped SLC1A3 SNPs .............................................................. 74!
Figure 4-2. Relative positions of the 10 genotyped SLC1A3 SNPs. ....................................... 75!
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List of Abbreviations 1H-MRS Proton magnetic resonance spectroscopy
ADHD Attention Deficit/Hyperactivity Disorder
AMPA α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionic acid
AMPAR α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionic acid receptor
APA American Psychiatric Association
ASP Affected sib-pair
CMT Chronic Multiple Tics
CSTC Cortico-Striatal-Thalamic-Cortical pathway
DNA Deoxyribonucleic acid
DSM Diagnostic and Statistical Manual of Mental Disorders
DZ Dizygotic
EAAT Excitatory amino-acid transporter
ENCODE Encyclopedia of DNA elements
EO Early-onset
FBAT Family-based association test
GRR Genotype relative risk
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Glx Glutamate + Glutamine
GLAST Glutamate aspartate transporter
GWAS Genome-wide association study
GWLS Genome-wide linkage scan
H3K4Me1 Mono-methylated lysine 4 of histone 3
H3K27Ac Acetylated lysine 27 of histone 3
HAT Histone acetyl-transferase
HWE Hardy-Weinberg equilibrium
K-SADS Kiddie Schedule for Affective Disorders and Schizophrenia
LD Linkage Disequilibrium
LO Late-onset
LOD Logarithm of odds
MAF Minor allele frequency
MLOD Multipoint maximum LOD score
MZ Monozygotic
NMDA N-methyl-D-aspartate
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NMDAR N-methyl-D-aspartate receptor
NPL Nonparametric LOD score
OCB Obsessive-compulsive behaviour
OCD Obsessive-compulsive disorder
PCA Principal components analysis
PCR Polymerase chain reaction
PIC Parent Interview for Child Symptoms
RNA Ribonucleic Acid
SDS Sequence Detection System
SLC1A1 Solute carrier family 1, member 1
SLC1A3 Solute carrier family 1, member 3
SNP Single nucleotide polymorphism
TSAICG The Tourette Syndrome Association International Consortium of Genetics
TDT Transmission disequilibrium test
TF Transcription factor
TFBS Transcription factor binding sites
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TS Gilles de la Tourette syndrome
TTI Teacher Telephone Interviews
UCSC University of California Santa Cruz
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Introduction Chapter 1
1.1 Introduction to Tourette syndrome
The ability to think or behave without interruption is important to quality of life and social
interaction (Cutler, Murphy, Gilmour, & Heyman, 2009; Eddy, Cavanna, Gulisano, Calì,
Robertson, & Rizzo, 2012; Packer, 2005). Generally, interruptions come from external
factors and to some degree can be controlled; however, there are circumstances in which
interruptions originate from the individuals themselves. When unwanted vocalizations or
motor movements result from sudden, recurrent and non-rhythmic movement of a muscle
group it is referred to as a tic (American Psychiatric Association, 2013). It is estimated that
15%-25% of school-aged children worldwide present with tic symptoms that last for less
than a year (Robertson, 2008). An additional 1% of children worldwide experience both
motor and vocal tics (Robertson, 2012). According to the Diagnostic and Statistical Manual
of Mental Disorders 5th ed. (DSM-V) when vocal and motor tic symptoms last for longer
than a year and are unexplained by other medical conditions or substance abuse, a diagnosis
of Gilles de la Tourette syndrome (TS) may be made (APA, 2013; see Table 1-1 for
overview of TS).
Tourette syndrome is a neurobehavioral disorder with an onset generally between 5
and 7 years of age (Robertson, 2012). Initial tic symptoms are often simple and involve a
single muscle group. For instance, common simple tics that result in vocalizations include
grunting and vocal clearing of the throat, and simple motor movements include eye blinking
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and shoulder shrugging. Given that these symptoms are mild it is not uncommon for them to
go undiagnosed and therefore untreated (Robertson & Gourdie, 1990). Symptoms of TS
generally become progressively more severe approaching puberty but often improve into
adulthood in approximately 80% of children and adolescence (Bloch & Leckman, 2009).
The course described above suggests that TS may result from neurodevelopmental
delays. This is in contrast to progressive disorders which are typically neurodegenerative and
become more severe with time ( reviewed in Paschou, 2013). Multiple magnetic resonance
imaging studies (Fahim, Yoon, Sandor, Frey, & Evans, 2009; Sowell et al., 2008; Worbe et
al., 2010) provide evidence of support for this hypothesis by demonstrating that the
maturation of brain regions that mediate self-regulation (e.g., basal ganglia and cortex)
coincide with the course of TS (Plessen, 2013; Gogtay et al., 2004). Despite mounting
evidence that alterations to brain regions such as the basal ganglia and cortex are involved in
the pathogenesis of TS, specific neuroanatomical or neurochemical alterations have yet to be
established in the majority of patients (reviewed in McNaught & Mink, 2011).
1.2 Evidence of a genetic basis of TS
To date, multiple family studies have demonstrated that the rate of TS is 10 to 100 times
greater in first-degree relatives of TS patients than in the general population (Kidd, Prusoff,
& Cohen, 1980; Kidd & Pauls, 1982; Pauls, Cohen, Heimbuch, Detlor, & Kidd, 1981; Pauls,
Kruger, Leckman, Cohen, & Kidd, 1984). While substantial clustering of TS within families
does not provide definitive evidence of a genetic basis it can be used along with twin studies
to gain a better understanding of a disorder and related traits.
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Given that monozygotic twins (MZ) share identical genetic code and dizygotic twins
(DZ) only share on average 50% of the genetic code, twin studies are commonly used to
provide evidence of a genetic basis of a particular disorder or trait. Assuming environmental
factors are similar for both twins, a higher concordance rate amongst MZ twins when
compared to DZ twins suggests a genetic component for the underlying trait. When the
concordance rate is below 100% for monozygotic twins, environmental factors are also likely
involved in susceptibility.
Price, Kidd, Cohen, Pauls, and Leckman (1985) conducted the first twin study of TS
using 43 pairs of same-sex twins. When using the narrow diagnostic criteria of TS (TS only),
concordance rates for monozygotic and dizygotic twins were 53% and 8%, respectively.
Consistent with these findings, Hyde, Aaronson, Randolph, Rickler, and Weinberger (1992)
also observed a concordance rate of 56% for 16 pairs of MZ twins. These studies provided
evidence that both genetic and environmental factors contribute to TS. It has since been
demonstrated that approximately 60% of phenotypic variability of TS is accounted for by
genetic factors (Davis et al., 2013).
1.3 Model of inheritance of TS
Complex segregation analyses from family studies of TS support a major locus contribution
(Comings et al., 1984; Curtis, Robertson, & Gurling, 1992; Eapen, Pauls, & Robertson,
1993; Hastedt et al., 1995; Pauls, et al., 1986, 1990; Seuchter et al., 2000; Walkup et al.,
1996). However, whether this major locus is inherited in an additive or autosomal dominant
fashion, and whether there is a polygenic contribution of smaller effect remains unclear.
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Despite multiple attempts to locate a definitive susceptibility gene of major effect none have
been identified.
1.4 Identification of genetic susceptibility factors
1.4.1 Linkage studies
Genetic linkage studies allow for the identification of broad chromosomal regions that may
harbor susceptibility genes (Teare & Barrett, 2005). In principle this type of study screens for
genetic loci that co-segregate with a particular phenotype amongst relatives (Borecki &
Suarez, 2001). Based on a priori information or lack of information of the trait of interest,
one of two linkage analysis strategies (e.g., parametric or non-parametric methods) can be
used to identify susceptibility loci.
Parametric or model-based linkage analysis is commonly used for Mendelian
disorders with a known disease-allele frequency, and affection status of parents and offspring
in large multigenerational families (Shih & Whittemore, 2001; Teare & Barrett, 2005). This
method is based on the principle that markers proximal to the disease-locus are less likely to
undergo recombination and therefore more likely to segregate within affected relatives than
unaffected relatives (Shih & Whittemore, 2001). The likelihood that the marker and disease-
locus are linked is often measured using the logarithm of odds (LOD) score, with a score of 3
or greater indicating significant evidence of linkage (Teare & Barrett, 2005).
Non-parametric or model-free linkage analysis allows for the identification of
chromosomal susceptibility regions of traits with an unknown mode of inheritance. This is
typically the case for common complex traits with multiple genes and environmental factors
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contributing to etiology (Teare & Barrett, 2005; Borecki & Suarez, 2001; Weeks & Lathrop,
1995). Genotypes of affected relatives with common ancestry (e.g., affected siblings) are
analyzed for identical allele or haplotype patterns found at a greater rate than expected by
chance (Teare & Barrett, 2005). These patterns are proposed to be indicative of susceptibility
regions for that particular trait or disorder.
Multiple genome-wide and targeted linkage studies of TS have been conducted using
parametric and non-parametric strategies (see Table 1-2 for summary of TS linkage findings).
Using these methods, over 15 chromosomal risk regions have been implicated. The lack of
definitive TS susceptibility regions may be due to a number of complications including
differing ethnic compositions between studies. Nevertheless, regions implicated by linkage
studies are broad and contain multiple plausible candidate genes. Therefore, linkage studies
alone do not provide good resolution for gene discovery (Teare & Barrett, 2005). Another
challenge of the linkage study is its poor resolution for identifying genetic variants with
small effect sizes (Cirulli & Goldstein, 2010). Therefore, while linkage studies are important
for the discovery of regions harboring potential risk genes, other strategies are also required
to identify genes conferring susceptibility.
1.4.2 Association studies
Association studies are used to determine whether a particular marker is associated with a
trait in a population (Borecki & Suarez, 2001). In contrast to linkage studies, genetic
association studies allow for targeted examination of plausible risk genes within linked
regions, and may be used to identify common risk alleles with small to modest effect sizes
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(Cirulli & Goldstein, 2010). Two types of association studies are commonly used i)
candidate gene association studies and ii) genome-wide association studies (GWAS).
Candidate gene association studies are hypothesis-driven, such that gene selection is based
on a priori knowledge. This includes selecting a candidate gene based on biological
relevance to a particular trait or localization within a susceptibility region. In contrast,
GWAS involve genotyping of an array of markers with common alleles and are used to
detect risk alleles with small to moderate effect across the genome without a priori
knowledge (Cirulli & Goldstein, 2010; Hindorff et al., 2009; McCarthy & Hirschhorn, 2008).
Advantages of conducting a GWAS include the identification of novel risk genes or risk
variants located within intergenic regions (Hindorff et al., 2009) that may have originally
been overlooked.
Genetic association studies can be approached as either case-control or family-based.
The case-control design uses two groups: i) cases, which are affected individuals and ii)
controls, individuals who are unaffected with the trait of interest. Case-control studies
determine association by comparing the allele frequencies between these two groups (Clarke
et al., 2011).
A major challenge with the case-control approach is that cases and controls may
differ in ethnicity (Curtis, 1997; Devlin & Roeder, 1999; Nicodemus, Luna, & Shugart,
2007). This is of concern because individuals of differing ethnic groups have different allele
frequencies, which can result in false-positive associations (Nicodemus et al., 2007). To
reduce confounding by population stratification, investigators attempt to select cases and
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controls from the same ethnic groups; however, even with careful selection, the effects of
population stratification may not be completely eliminated (Clarke et al., 2011). To address
this limitation, studies involving large number markers, such as GWAS, implement statistical
tools that can make inferences about population structure from genotype data. Individuals
that do not sufficiently match up are removed from the study, improving the ancestral-
homogeneity of the sample. Principal-component analysis (PCA) and multidimensional
scaling (MDS) are two commonly used statistical strategies to describe the data using fewer
variables (Patterson, Price, & Reich, 2006; Price et al., 2008). Population stratification can
also be addressed by using genotypes of relatives (e.g., parents or siblings), rather than
unrelated cases and controls.
Recently the first GWAS of TS was conducted to identify common risk variants
(Scharf et al., 2012). In this study 496,877 SNPs were tested for association in 1285 TS
cases and 4964 European-matched controls (Scharf et al., 2012). Despite using the largest TS
sample to date, no markers reached the genome-wide threshold of significance (p < 5 x 10-8,
Scharf et al., 2012). The strongest signal from the primary analysis of this study was from an
intron of COL27A1 (p = 1.85 x 10-6), which is localized on chromosome 9q32. COL27A1
encodes for a fibrillar collagen and is expressed in multiple brain regions; however,
according to Fox (2008) the involvement of this gene in the development of the central
nervous system remains unclear. Scharf and colleagues (2012) also conducted a secondary
analysis that included 211 TS cases and 285 controls of Latin-American descent. The
expansion of the sample to include affected individuals of Latin-American descent did not
improve efforts to identify significant associations with TS. Given that previous GWAS of
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other psychiatric disorders (e.g., bipolar disorder and schizophrenia) required larger sample
sizes to reach the threshold for genome significance (Mühleisen, Leber, Schulze, &
Strohmaier, 2014; Schizophrenia Psychiatric Genome-Wide Association Study Consortium,
2011; Sullivan et al., 2012) it has been proposed that this study was underpowered (Scharf et
al., 2012). Therefore, while no genes met the threshold of genome-wide association, variants
with sub-threshold p values may reflect true associations.
Multiple candidate gene association studies have been conducted, implicating genes
of various pathways. Two of the most commonly studied genes include DAT1 (Yoon et al.,
2007; Comings et al., 1996) and SLITRK1 (Stillman et al., 2009; Abelson et al., 2005;
Miranda et al., 2009; O'Roak et al., 2010; Karagiannidis et al., 2012). Although it is possible
that these genes are involved in TS susceptibility, the risk alleles of these genes alone do not
account for TS susceptibility.
1.4.3 Linkage Disequilibrium
Recombination events over generations produce new combinations of alleles. Depending on
genomic distance, certain chromosomal sites are more likely to undergo recombination than
others. This gives rise to alleles at the same loci being more or less likely to be inherited
together. When the association of alleles at two or more loci is nonrandom it is referred to as
linkage disequilibrium (Pritchard & Przeworski, 2001). Genetic association studies of disease
rely on this property to map genes that cause disease. For this particular investigation LD
was used to systematically select SNPs for association testing with TS and ADHD in the
respective samples.
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1.4.3.1 Measurements.of.Linkage.Disequilibrium.
There are multiple statistics used to describe the LD between marker pairs, however, two
frequently used measures are i) the Lewontin D’ statistic and ii) the squared correlation
coefficient, r2 (Mourad, Sinoquet, Dina, & Leray, 2011; Delvin & Risch, 1995).
The Lewontin D’ is a standardized function based on the measure D, the difference
between the observed and expected haplotype frequencies (Mueller, 2004). Lewontin D’ is
derived by dividing D by its maximum value, Dmax and evaluates the independence of two
markers given maximum linkage (Lewontin, 1964). Alleles at two loci with little distance in-
between are more likely to have little to no historical recombination. This relationship is
indicated by a D’ value approaching 1. When D’=1 it is called complete LD. When this is the
case, given the allele frequencies, the correlation between alleles is described as strong as
possible (Carlson et al., 2004). As genetic distance increases, the strength of correlation
between these markers decreases due to recombination throughout generations. Frequent
recombination between markers of two loci on the same chromosome would be represented
by a D’ value approaching 0.
The squared Pearson’s correlation coefficient, r2, similar to D’, may be used to assess
the correlation between two markers. To gain a better understanding of r2 consider two
biallelic loci on the same chromosome. The first locus consists of alleles A and a and the
second locus consists of alleles B and b (common and minor alleles, respectively). Based on
recombination, the possible haplotype combinations include AB, Ab, aB and ab. r2 is
computed with the following equation:
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, where p is frequency
Equation (1) shows that although two markers may be correlated, the strength of LD
is reduced if the allele frequencies greatly differ (Edge, Gorroochurn, & Rosenberg, 2013).
When the allele frequencies are the same and when r2=1, the relationship between markers is
described as perfect LD (Reich et al., 2001). Generally, the LD between two loci is rarely
perfect, as allele frequencies at two loci often differ (Edge, Gorroochurn, & Rosenberg,
2013). Similar to D’, when r2=0 it indicates independence: information regarding one allele
does not provide information for another allele. When alleles are inherited independently
from one another it is referred to as linkage equilibrium.
Ultimately the use of these measurements depends on the context (Pritchard &
Przeworski, 2001). D’ is often used to compare LD between markers in different populations
(Reich et al., 2001), whereas r2 is better suited for association studies (e.g., tagSNP selection).
1.5 The Behavioral Spectrum of TS
Failure to identify many risk alleles is not due to a lack of susceptibility regions. On the
contrary, linkage studies of TS have implicated multiple regions on at least 10 different
chromosomes. While these findings in addition to family and twin studies, support the
involvement of genetic factors they are problematic: many of these linkage findings have yet
to be replicated or have only produced weak evidence of linkage. Furthermore, there are
hundreds of genes within these regions, such that it would be difficult to sufficiently narrow
the search for genes conferring risk.
€
r2 =pAB − pA pB( )2
pA pB pa pb
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The etiologic complexity of TS likely plays a role in the challenge of identifying
specific risk genes. From a clinical perspective, individuals affected with TS vary in
presentation (e.g., severity, age of onset, symptom types). This, in addition to the number of
highlighted linkage regions, suggests that TS may not only be clinically heterogeneous but
also genetically heterogeneous. To clarify current genetic findings of TS, investigators have
searched for more homogeneous subgroups. To date, at least three subgroups have been
identified including: i) TS+ obsessive compulsive behavior, (OCB) the less severe form of
OCD that does not meet the impairment criteria ii) TS+OCD and iii) TS+OCD+ADHD
(Alsobrook & Pauls, 2002; Grados et al., 2008; Mathews et al., 2007; Robertson, et al., 2007,
2008b). One possibility is that there is variable expressivity of TS risk genes, such that these
subgroups represent more severe manifestations of TS, each with common and unique risk
factors (Grados et al., 2008; Mathews & Grados, 2011; Pauls, et al., 1986, 1990, 1992).
Under this assumption of expressivity, additional variables including gene-environment
interactions and non-genetic factors may also extend the behavioral spectrum to also include
milder forms of TS. Therefore, consideration of the behavioral spectrum of TS may improve
efforts to identify additional risk alleles.
1.5.1 Chronic Multiple Tics
Chronic multiple tics (CMT) is a childhood-onset psychiatric disorder characterized by the
presence of either motor or vocal tics for at least a year (APA, 2013). Family studies of TS
demonstrate that the rate of CMT amongst first-degree relatives is 5- to 20- fold greater when
compared to the general population (Pauls, Cohen, Heimbuch, Detlor, & Kidd, 1981; Pauls,
Raymond, Stevenson, & Leckman, 1991). While positive familial aggregation of CMT
12
within families of TS patients does not provide definitive evidence of a genetic basis,
broadening the diagnostic criteria to include CMT (and other tic disorders) in a twin study of
TS increased partial concordance rates from 53%-56% to 77%-94% in monozygotic twins,
and 8% to 23% in dizygotic twins (Hyde et al., 1992; Price et al., 1985). Based on the
evidence, CMT is believed to represent a less severe manifestation of TS (Pauls, et al., 1981,
1990; Price et al., 1985).
1.5.2 TS comorbidities and potential TS subgroups
Multiple neuropsychiatric disorders have been found at higher rates in relatives of TS
patients when compared to rates in the general population. It is estimated that 90% of
individuals affected with TS also have additional psychiatric or behavioral conditions
(Freeman et al., 2000). TS with comorbid obsessive-compulsive disorder (OCD) and/or
attention-deficit/ hyperactivity disorder (ADHD) are proposed to represent potential subtypes
of the TS spectrum. The relationship between TS and comorbid OCD and ADHD are
discussed in more detail below.
1.5.3 Comorbid TS with OCD
OCD is heritable psychiatric disorder (van Grootheest, Cath, Beekman, & Boomsma, 2005;
Hettema, Neale, & Kendler, 2001) characterized by obsessions and/or compulsions that are
time consuming or result in marked distress, impairment , or are difficult to ignore (APA,
2013; see Table 1-1 for overview of OCD). Obsessions are any persistent, unwanted and
intrusive thoughts, images or urges (APA, 2013). Compulsions are excessive or repetitive
behaviours or mental acts that typically reduce anxiety brought on by obsessions (APA,
13
2013). Compulsions may also include any behaviors or mental acts that are believed to
prevent or lessen an unrelated event (APA, 2013).
Two distinct onsets have been observed in OCD patients: early-onset and late-onset.
Symptoms of early-onset OCD (also referred to as childhood- or pediatric- OCD) generally
begin around 10 years of age. Clinical and epidemiology studies of OCD have found that
males are four times more likely to be diagnosed with early-onset OCD than females. While
childhood-OCD describes a subgroup of individuals affected with OCD before the age of 18,
late onset (also known as adult-onset) OCD describes individuals with OCD whose
symptoms begin at or after 18 years of age. In addition to differences in age of onset,
evidence from family studies indicate that early-onset OCD is more heritable than late-onset
providing further support that these onsets may have unique and overlapping etiological
factors (van Grootheest et al., 2005; Davis et al., 2013).
The estimated worldwide prevalence of OCD is 2.5% (Ruscio, Stein, Chiu, & Kessler,
The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey
Replication, 2010) yet clinical-based epidemiology studies show that up to 66% of TS
patients are also affected with OCD (Freeman et al., 2000), and up to 80% of TS patients
present with OCB (Pauls et al., 1986). Considering that these rates are substantially higher in
individuals with TS than the general population (Pauls, Alsobrook, Goodman, Rasmussen, &
Leckman, 1995), it has been proposed that TS and some phenotypes of OCD share
etiological factors (Eichstedt & Arnold, 2001; Mathews & Grados, 2011; Pauls, et al., 1986,
1995). Symptom dimensions of OCD that are related to TS include obsessions involving
14
symmetry, and compulsions involving touching, order, repetition, arranging and counting
(Bloch & Leckman, 2009; Grados & Mathews, 2009; Leckman et al., 2003; Rizzo, Gulisano,
Calì, & Curatolo, 2012). Other studies have also found that early-onset OCD (Hemmings et
al., 2004), as well as subclinical OCD (OCB) are also frequently associated with TS (Eapen
et al., 1997).
1.5.4 Evidence of genetic overlap between TS and OCD
Evidence of shared genetic susceptibility comes from family and twin studies of large TS
families. These studies show that TS and certain symptom dimensions of OCD co-segregate
within families (Curtis, Robertson, & Gurling, 1992; Grados et al., 2001; McNaught & Mink,
2011; Pauls et al., 1990). Other family studies report increased incidence of OCD (with or
without tics) amongst relatives of TS probands (Pauls et al., 1991). The rate of TS (with or
without OCD) amongst families of OCD probands has also been found to be significantly
greater than expected by chance (Pauls & Leckman, 1986). These rates were observed
regardless of a proband diagnosis of comorbid TS or OCD (Eapen et al. 1993; Pauls, et al.,
1986, 1991; Pitman, Green, Jenike, & Mesulam, 1987).
Within recent years multiple bivariate analyses have been conducted to uncover the
genetic correlation between TS and OCD. These studies have demonstrated heritability
estimate of 0.40, suggesting that genetics account for some of the overlap observed between
TS and OCD (Davis et al., 2013). Given the evidence of genetic overlap between phenotypes
of OCD and TS (Pauls, et al., 1984, 1986, 1991; Price et al., 1985; Walkup, Leckman, Price,
Hardin, Ort, & Cohen, 1988), a recent cross- disorder GWAS consisting of TS and OCD
15
cases, aimed to gain a better understanding of the genetic etiological architecture between TS
and OCD. Although no genetic variants reached the threshold for genome-wide association
(p= 5x 10-8), additional analysis provided support that OCD associated with TS (TS+OCD) is
likely genetically distinct from OCD only (Davis et al., 2013).
Several overlapping brain regions have also been implicated in both TS and OCD.
For instance, neuroimaging studies of children with TS and OCD demonstrate altered
volume or activation of the striatum (Albin & Mink, 2006; Benedetti et al., 2012; Hoexter et
al., 2012; Peterson, et al., 2000, 2003; Pujol et al., 2004; Zarei et al., 2011) and cerebellum
(Bohlhalter et al., 2006; Pujol et al., 2004; Zarei et al., 2011). Despite this evidence, specific
neuroanatomical alterations involved in TS, with or without OCD, have yet to be identified.
1.5.5 Comorbid TS and ADHD
ADHD is a childhood-onset psychiatric disorder characterized by persistent inattentive or
hyperactive-impulsive behaviour that is inappropriate for one’s developmental age and
results in impaired functioning (APA, 2013; see Table 1-1 for overview of ADHD). The
onset for ADHD is prior to the age of 12 (APA, 2013) with motor hyperactivity generally
preceding inattentiveness or cognitive hyperactivity-impulsivity. As with other
neurodevelopmental psychiatric conditions, clinical presentation of ADHD is dynamic and
commonly changes into adolescence and adulthood (APA, 2013; Turgay et al., 2012).
ADHD affects an estimated 5%-12% children worldwide (Biederman & Faraone,
2005; Polanczyk, et al., 2007a, 2007b). As noted earlier, ADHD is frequently comorbid with
TS, with clinical-based prevalence estimates around 55% (Freeman & Consortium, 2007;
16
Ludolph, Roessner, Münchau, & Müller-Vahl, 2012) and community-based reports around
38% (Pringsheim & Hammer, 2013). This discrepancy may be due to the fact that comorbid
ADHD is associated with more severe deficits, resulting in increased likelihood of referral to
clinics. Nevertheless, the prevalence of ADHD is substantially greater than expected in both
clinical and population based settings.
Early family studies suggest that ADHD represents an alternative phenotype of TS
risk genes (Comings, et al., 1984, 1987, 2001; Knell & Comings, 1993); however, more
recent findings indicate a more complex relationship (Grados et al., 2008; Pauls, et al., 1986b,
193; Stewart et al., 2006; Mathews & Grados, 2011). It has since been proposed that TS and
ADHD are not manifestations of the same genetic risk factors; rather the purest forms of TS
and ADHD likely represent distinct entities with different genetic factors (Murphy & Muter,
2012; Stewart et al., 2006). Further support for this hypothesis comes from the lack of
evidence of a genetic correlation between TS and ADHD when no other conditions are
considered (Mathews & Grados, 2011). However, there is some evidence indicating that in
some cases the co-occurrence of TS and ADHD may be mediated by OCD. When found in
the same individual TS, ADHD, and OCD (TS+ADHD+OCD) has been described as highly
heritable (Grados et al., 2008) and informative of predicting offspring diagnosis from
parental diagnosis (Mathews & Grados, 2011). It is plausible that TS+ADHD+OCD is a
manifestation with additive overlapping genetic risk factors of TS, OCD and ADHD.
17
1.5.6 Relevance
In contrast to TS, which infrequently results in impaired functioning (APA, 2013), comorbid
TS with ADHD and/or OCD is often associated with impaired social functioning, self-value
and ability to thrive in a work or academic environment (Conelea et al., 2011; Eddy et al.,
2012; Pringsheim, Lang, Kurlan, Pearce, & Sandor, 2009; Rizzo et al., 2007). Moreover, TS-
affected individuals with additional diagnoses are also more likely to be perceived as being
deliberate disruptive and have a reduced quality of life (Debes, Hjalgrim, & Skov, 2009;
Eddy et al., 2012; Hassan & Cavanna, 2012). Efforts to treat TS only, ADHD only, and OCD
only are currently subpar. It is therefore not unexpected that there are no definitive
treatments for individuals with any combination of these disorders. Due to the lack of
specific treatment strategies, current therapeutic-drug efforts often result in exacerbation of
comorbid symptoms (Lombroso & Scahill, 2008). Refinement of the TS phenotype into
subgroups may not only give researchers a better understanding of the mechanisms
underlying TS, but may also guide the exploration of therapeutics of these comorbid
disorders.
1.6 The Glutamatergic System
Glutamate is the main excitatory neurotransmitter in the mammalian central nervous system
(Kanai et al., 2013). Stored in vesicles, glutamate is released at the presynaptic bouton in
response to influx of calcium ions. At the postsynaptic cleft, glutamate mediates excitatory
signal transduction through binding to one of two classes of glutamate receptors: i) ligand-
gated ion channels (e.g., ionotropic receptors) or ii) G-coupled proteins (metabotropic
receptors). Metabotropic receptors are involved in slow transmission and long- term changes
18
(reviewed in Riedel, Platt, & Micheau, 2003). In contrast, ionotropic receptors mediate fast
transmission and facilitate the transport of Na2+ and K+ ions. Three ionotropic receptors exist:
N-methyl-D-aspartate (NMDA), α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionic acid
(AMPA) and kainite. Given that AMPA receptors (AMPAR) and kainite receptors can be
activated by the same agonists, these receptors are collectively classified as non-NMDA
receptors (Ozawa, Kamiya, & Tsuzuki, 1998).
The activation of AMPA and NMDA receptors (AMPAR and NMDAR, respectively)
mediate synaptic changes underlying behaviour by a process known as synaptic plasticity
(Ozawa et al., 1998). Glutamate binding to the AMPAR results in excitatory postsynaptic
potentials on the postsynaptic neuron, causing the removal of Mg2+ from the NMDAR ion
channel (Clements & Westbrook, 1991; Singer, Morris, & Grados, 2010). This allows for
extracellular glutamate- and glycine- (or D-serine) binding of the NDMAR, causing further
depolarization and increasing calcium permeability into the postsynaptic neuron. Within the
neuron, calcium ions act as a second messenger, mediating a cascade of intracellular
signaling involved in altering the surface of neurons (Purves et al., 2001). The role of
glutamate in synaptic plasticity is thus important in the maturation of synapses and ultimately
the development of the central nervous system (Ozawa et al., 1998; Riedel, Platt, & Micheau,
2003).
Despite playing a critical role in the development of normal brain function (Zhou &
Danbolt, 2013), when found in excess, glutamate can also act as a neurotoxin via
excitotoxicity (Ozawa, Kamiya, & Tsuzuki, 1998). During this process, excessive activation
19
of the NMDAR allows for aberrant concentrations of calcium into the postsynaptic neuron
(Purves et al., 2001). As a result calcium-dependent enzymes such as proteasomes become
active and degrade essential cell proteins causing cell death (Brennan-Minnella, Shen, El-
Benna, & Swanson, 2013).
Due to the implications of inappropriate glutamate concentrations in the brain, synaptic
glutamate is regulated by excitatory amino-acid transporters (EAATs), which are localized
on either glia cells or neuron. Alterations to the localization or capacity of these transporters
may in turn disrupt synaptic signaling (Tzingounis & Wadiche, 2007). As a result the
expression of glutamate transporters is critical for maintaining appropriate glutamate levels
within synapses (Portera-Cailliau, Price, & Martin, 1997).
1.7 The Glutamatergic Hypothesis
Although no definitive neuroanatomical localizations of TS have been discovered, multiple
lines of evidence suggest that abnormal synaptic neurotransmission of the cortico-striatal-
thalamic-cortical (CSTC) pathways underlies TS pathophysiology (Singer et al., 2010;
reviewed in Stern, Blair, & Peterson, 2008). These pathways are proposed to influence
behaviors for motor function, impulsivity, attention and executive function (Stahl, 2009;
Udvardi, Nespoli, Rizzo, Hengerer, & Ludolph, 2013). Given the involvement of glutamate
in excitatory neurotransmission, synaptic plasticity and excitotoxicity, aberrant activity in
regions associated with the CSTC circuits may result from dysregulation of the glutamatergic
system. To date, abnormal activity and glutamate concentrations within the CSTC have been
implicated in a variety of neurodevelopment and neurodegenerative disorders including
20
Huntington’s disease (Behrens, Franz, Woodman, Lindenberg, & Landwehrmeyer, 2002),
Alzheimer’s disease (Francis, 2003), epileptic seizures (Tanaka et al., 1997) and autism
spectrum disorders (Choudhury, Lahiri, & Rajamma, 2012). The following section will
overview findings from rodent models, functional imaging studies, pharmacological studies
and genetic studies that support the hypothesis that dysregulation of the glutamatergic system
in the CSTC may contribute to TS and its related disorders, ADHD and OCD.
1.7.1 Evidence implicating the glutamatergic system in TS
To date, only one proton magnetic resonance spectroscopy (1H-MRS) study of brains of
individuals with TS has been conducted (DeVito et al., 2005). While DeVito and colleagues
were unable to detect abnormal glutamate + glutamine (glx) concentrations in the frontal
cortex, striatum or thalamus there is mounting evidence from other lines of study suggesting
the involvement of abnormal glutamate neurotransmission in TS. For instance, reduced
glutamate concentrations in the globus pallidus have been observed in post-mortem brains of
TS patients (Anderson et al., 1992). Engineered using a transgene encoding for D1 dopamine
receptor neurons with enzymatic cholera toxin, D1CT-7 mice models have also implicated
abnormal glutamatergic transmission in TS (McGrath, Campbell, Parks, & Burton, 2000;
Nordstrom & Burton, 2002). Nordstrom and Burton (2002) observed striatal hyperactivity
from cortical glutamatergic projections in D1CT-7 mice. Exacerbation of tic symptoms (e.g.,
increased number of tics and decreased tic responsiveness to drug therapy) in D1CT-7 mice
has also been observed following administration of glutamatergic drugs (McGrath et al.,
2000). Current pharmaceutical efforts are also investigating anti-glutamatergic drugs (i.e.,
21
riluzole and N-acetylcysteine) as possible treatment of tics (Singer, 2010; Udvardi et al.,
2013).
1.7.2 Evidence implicating the glutamatergic system in ADHD
Altered glx concentrations in the brains of individuals affected with ADHD have been
reported by multiple studies using 1H-MRS (Perlov et al., 2009). One 1H-MRS study
revealed a significant decrease in the glutamate/glutamine/GABA to
creatine/phosphocreatine ratio in the striatum following treatment with ADHD medication
(Carrey et al., 2003). In another study using 1H-MRS, striatal glx concentrations were also
found to be low in ADHD patients (Maltezos et al., 2014). These studies in addition to others
have suggested that hypoactivation of glutamatergic projections extending to the striatum
may be involved in ADHD pathology; however, opposing findings have also been reported.
For instance, Carrey and colleagues (2007) also conducted a 1H-MRS study, but observed
greater, not lower, striatal glx concentrations in the brains of individuals with ADHD when
compared to controls. Furthermore, hyperactive glutamate terminating in the prefrontal
cortex and the striatum has also been observed in spontaneously hypertensive rats, a rodent
model of ADHD (Miller, Pomerleau, Huettl, Gerhardt, & Glaser, 2014). Finally, genes
involved in the glutamatergic system including GRIN2B (Dorval et al., 2007) and SLC1A3
(Elia et al., 2009; Turic et al., 2005) have also been implicated in ADHD.
1.7.3 Evidence implicating the glutamatergic system in OCD
Abnormal glutamate signaling has been hypothesized to contribute to OCD pathology
(reviewed in Macmaster & Rosenberg, 2010). A neuroimaging study using 1H-MRS
22
observed higher glx concentration in the striatum of OCD patients when compared to
controls (Rosenberg & Keshavan, 1998). In a subsequent study, Rosenberg and colleagues
found that striatal glx decreased following treatment with paroxetine (Rosenberg et al., 2000),
an accepted drug used to treat OCD symptoms. Considering the evidence, glutamatergic
drugs have been proposed to have therapeutic influence in OCD patients (Coric et al., 2005;
Pasquini & Biondi, 2006; Poyurovsky, Weizman, Weizman, & Koran, 2005). In humans,
Chakrabarty (2005) observed increased glutamate levels in the cerebrospinal fluid of OCD
patients. Multiple genetic studies of OCD have also implicated genes involved in the
glutamatergic system including SLC1A1 (Arnold, Sicard, Burroughs, Richter, & Kennedy,
2006; Dickel et al., 2006; Samuels et al., 2011; Shugart et al., 2009; Stewart et al., 2007;
Porton et al., 2013; Wendland et al., 2009) and GRIN2B (Arnold et al., 2004).
1.8 Genetic evidence of overlap between TS, ADHD and OCD
1.8.1 Common susceptibility loci of TS, ADHD and OCD
Evidence from genome-wide and targeted linkage scans have implicated multiple
chromosomal loci in TS, ADHD and OCD; however, no susceptibility region has been linked
to all three disorders. Reports of chromosomal abnormalities in addition to regions with
positive or suggestive findings from linkage studies may provide clues to chromosomal
regions harboring susceptibility genes involved in all three disorders. Using linkage and case
reports, two regions, chromosome 5p13 and 9p24, have been selected for further study in the
present investigation.
23
1.9 Genetic studies in candidate region 9p
1.9.1 Linkage studies implicating 9p in OCD
Multiple susceptibility regions have been identified for OCD; however, only linkage to
region 9p has been replicated (Hanna et al., 2002; Willour et al., 2004). Genome-wide
linkage scans of seven multigenerational families affected with early-onset OCD found
suggestive linkage to region 9p using a dominant model of inheritance (LOD=2.25; Hanna et
al., 2002). Fine mapping narrowed this region to marker D9S288, which is located at region
9p24. A subsequent linkage study using an independent sample targeted this region with the
objective of replicating these findings (Willour et al., 2004). The strongest signals came from
markers D9S1792 and D9S1813, two markers in proximity with the region highlighted in the
Hanna et al. study.
1.9.2 Case reports of 9p chromosomal abnormalities in TS
Two case reports have implicated chromosome 9p in TS (Taylor et al., 1991; Singh, Howe,
Jordan, & Hara, 1982). The first case involves a woman with a triple X mosaicism and a
deletion of the 9p region. Clinical features present in this woman included seizure, mental
retardation and TS (Singh et al., 1982). A second case implicating 9p was observed in 1991
by Taylor et al. This report describes an adolescent male with developmental delays who was
also affected with TS and OCD. Further evaluation of this subject’s chromosomal
architecture revealed a partial deletion of 9p of the terminal region.
24
1.9.3 Candidate gene studies at 9p24
Given the likely role of the glutamatergic pathway in the etiology of TS, ADHD and OCD,
genes involved in the glutamatergic system are of particular interest. Of the 41 genes located
within the 9p24 region, two encode for proteins expressed in the brain (Stewart et al., 2013).
One of these genes encodes for a glutamate transporter, SLC1A1. The following section
reviews relevant scientific publications of this SLC1A1 gene.
1.10 SLC1A1
Solute carrier family 1, member 1 (SLC1A1; also known as EAAT3 or EAAC1) is a
postsynaptic excitatory amino acid transporter that is involved in glutamate clearance from
the extracellular space. In the brain, SLC1A1 is enriched in the cortex, hippocampus,
cerebellum and the striatum (Guillet et al., 2005; Furuta et al., 1997; Maragakis & Rothstein,
2004; Rothstein, 1994), although analyses of rodent brains have shown that eaac1 expression
and distribution vary with CNS development (Furuta, et al., 1997a, 1997b).
1.10.1 SLC1A1 genetic studies in OCD
Arnold and colleagues (2006) conducted a family-based association study using DNA from
157 families with at least one OCD affected child. Two single-nucleotide polymorphisms,
rs301434 and rs301435, were found to be significantly associated in males only (additive
model; p = 0.006 and p = 0.03, respectively). Using a haplotype analysis, two-marker
haplotype rs301434/rs3087879, was also found to be significantly associated with males (p =
0.006).
25
In another study nine SNPs were tested for association in 71 OCD affected children
(Dickel et al., 2006). Four of the nine polymorphisms had been genotyped by Arnold and
colleagues (rs3780412, rs301430, rs301979, rs301434). Unlike findings from the Arnold et
al. study, the most positive associations were found for markers rs3780412 and rs301430 (p
= 0.04 and p = 0.03, respectively). Additional analysis by sex revealed that marker
rs3780412 association was limited to male probands (p = 0.002). A different two-marker
haplotype rs301430/rs301979 was also found to be nominally associated with this sample (p
= 0.03). This association was limited to males (p = 0.003). In addition to investigating single
locus and haplotype polymorphisms, Dickel and colleagues (2006) also reported a deletion at
the 3’ flanking region of the SLC1A1 gene (chr9:4,594,839-4,594,849, hg19) that segregated
with the OCD trait in a large multigenerational family.
Stewart and colleagues (2007) conducted the next SLC1A1 association study in OCD
using 66 probands (38.6% with comorbid TS); however, the investigation by Stewart et al.
failed to detect significant association for 6 previously OCD associated SNPs (e.g.,
rs3780412, 301430, rs301434, rs301435, rs3087879, rs301979). Only markers rs3780412
and rs2228622 showed a trend towards significance in affected males (p = 0.045) (Stewart el
al., 2007). Furthermore, three-marker haplotype rs12682807/rs2072657/rs301430 spanning
2.7kb, showed a trend with the OCD sample (p = 0.0031). A later study of SLC1A1 reported
association of single marker rs12682807 in OCD-affected males (Stewart et al., 2013).
Many of the variants associated with OCD have no known function. Given that
regulators of a gene may be located outside of the gene, Shugart et al. (2009) tested variants
26
flanking SLC1A1, in addition to conducting a replication study of previously genotyped
SNPs. In the Shugart study mentioned above, thirteen variants were tested for association
using a sample of 378 affected probands. Only one variant, rs301443 showed significant
association to OCD (p = 0.000067; Bonferroni corrected p = 0.0167); however, this
association was limited to male probands (p = 0.00027). Support of association for this
marker has also been reported by a large meta-analysis, although this finding did not
withstand correction for multiple testing (Stewart et al., 2013). Previous association studies
(Arnold et al., 2006; Dickel et al., 2006) were unable to detect an association with this SNP,
nor was this SNP associated in another study using a large sample consisting of 1576
subjects in 377 OCD affected families (Samuels et al., 2011). Samuel and colleagues were
also unable to identify any common SNPs in LD with rs301443 and were unable to detect an
association of these markers with OCD.
Wendland et al. (2009) used 325 OCD probands to test 4 SNPs that had been
previously genotyped in OCD samples (rs378412, rs301430, rs301434, and rs3087879), in
addition to two tagSNPs that had been yet to be studied in OCD (rs3933331 and rs7858819).
This investigation reported a significant association for a single-marker rs3087879 (p =
0.02); however, after correcting for multiple testing this marker was no longer associated (p
= 0.12). Marker rs3933331 was significantly associated with hoarding, and markers
rs7858819 and rs301430 were nominally significantly associated with SLC1A1 expression
levels in the brain. Wendland and colleagues also found that decreased SLC1A1 expression
was correlated with increased number of T alleles at both of these variants. Haplotype
analyses were also conducted: two haplotypes for rs7858819/rs301430/rs3087879 were
27
significantly associated with OCD, and remained significant after corrections (p < 0.001 and
p = 0.002). These findings support the hypothesis that a causal variant is located within the 3’
end of SLC1A1.
Despite encouraging results by Wendland and colleagues, screening of coding
regions of SLC1A1 have not identified functional variants that may account for previous
association findings in OCD (Veenstra-VanderWeele, et al., 2001, 2012).
1.10.2 Rationale for investigating SLC1A1 in TS and ADHD
As previously indicated, OCD is genetically correlated with both TS and ADHD (Mathews
& Grados, 2011). Putative OCD risk genes that are also strong functional candidates for TS
and ADHD may be used to guide further investigations. SLC1A1 has been associated with
OCD in multiple studies, and is a functional candidate for TS and ADHD. Despite being a
robust candidate, there are no published studies of the association of this gene with TS and
ADHD.
1.11 Genetic studies in candidate region 5p13
1.11.1 Linkage studies implicating 5p13 in ADHD
The first ADHD genome-wide linkage scan (GWLS) consisted of 104 American families
(Fisher et al., 2002). In the GWLS conducted by Fisher and colleagues, modest evidence of
linkage (LOD>2, p = 0.0009) was found for region 5p12. Two follow up GWLS were
conducted, which used the original ADHD sample and additional families. The first study
was conducted by Ogdie et al. (2003) used a sample of 204 nuclear families with at least two
ADHD affected children. The second study used a sample that was expanded to include 308
28
families (Ogdie et al., 2004). Both studies found weak evidence implicating region 5p13
(MLS>1, p = 0.002 and MLS= 2.55, p = 0.091; Ogdie, et al., 2003, 2004, respectively).
Using a Dutch sample of 164 sib-pairs with ADHD, the results of Bakker et al. (2003) also
yielded modest but supporting evidence of linkage (mMLS= 1.43) to 5p13 under the broad
phenotype. Using a pooled GWLS of the studies mentioned above, chromosome 5p13 was
the only region to be highlighted (Ogdie et al., 2006). Finally, from a GLWS in a German
sample of 155 sib-pairs, the strongest nonparametric multipoint signal (LOD score = 2.59)
also came from chromosome 5p (Hebebrand et al., 2006). Considering the overlap in reports
for chromosome 5p, and more specifically for region 5p13, this region is of interest regarding
ADHD susceptibility.
1.11.2 Linkage studies implicating 5p13 in TS
The chromosome 5p region has also been implicated in TS. Barr and colleagues (1999)
performed a GWLS using seven large multigenerational families. Although no markers
reached statistical significance levels under the autosomal dominant model, multiple markers
yielded LOD scores above 1. Additional fine mapping of regions with LOD > 1 highlighted
two chromosomal regions in some of these large multigenerational families. One of the
highlighted regions spanned chromosome 5p13-q11.2. Although these findings were
suggestive, it provided a basis for future genetic studies in TS. A follow up study by Laurin,
Wigg, Feng, Sandor and Barr (2009) used an extended sample consisting of eleven additional
family members and fine mapping of the 5p region. The results of this study highlighted the
same region of interest as Barr et al. Further, another dependent study that included the
multigenerational families examined by Barr et al. and Laurin et al. (2009), also observed
29
suggestive evidence of linkage to the 5p13 region (LOD>2) (The Tourette Syndrome
Association International Consortium for Genetics, 2007).
Additional evidence implicating chromosome 5p in TS pathophysiology comes from a
recent investigation that partitioned heritability by chromosomal length (Davis et al., 2013).
This study revealed that chromosome 5 might account for more TS heritability than expected
when compared to other chromosomes (Davis et al., 2013). However, these findings are
recent and this study has yet to be replicated.
1.12 SLC1A3
SLC1A3 (also known as excitatory amino acid transporter 1, EAAT1 or GLAST) is a
glutamate transporter gene located on chromosome 5p13 (Kanai et al., 2013). This gene
encodes for a high affinity sodium-dependent glial glutamate transporter, SLC1A3, that is
predominately involved in synaptic glutamate clearance (Rothstein et al., 1996). Following
the uptake of synaptic glutamate by SLC1A3, glutamate is converted by glutamine-synthase
into its metabolite, glutamine (Udvardi et al., 2013). Glutamine is then shuttled into the
presynaptic neuron where it is converted back into glutamate and repackaged into vesicles
(Udvardi et al., 2013).
1.12.1 SLC1A3 studies of ADHD
Turic et al. (2005) conducted an association study of SLC1A3 using 299 ADHD-
affected families. One marker, rs2269272 (p = 0.007), and two-marker haplotypes,
rs2269272-rs3776581 (p = 0.016) and rs2269272-rs2032893 (p = 0.013) showed weak
evidence of association. A later study in ADHD conducted by Elia and colleagues (2009)
30
observed associations for other genetic variants spanning SLC1A3: an intronic SNP
(rs3776571) and two downstream variants rs1529461 and rs6863386. A large meta-analysis
of ADHD genome-wide association studies (Neale et al., 2010) observed association for one
of these markers (rs1529461=0.00475), although this association signal did not reach the
threshold for significant genome-wide association.
In 2008, our lab conducted a replication study of findings from Turic et al. using a
sample of 245 nuclear families with at least one ADHD affected child (total of 280 affected
children)(Laurin et al., 2008). Although we were unable to find a significant association
between the three markers of interest (rs2269272, rs3776581 and rs2032893) it would take at
least 27 tagSNPs to cover SLC1A3. Since these studies, no additional genetic studies have
been published regarding the relationship between SLC1A3 and ADHD.
1.12.2 SLC1A3 studies of TS
Based on the overlap in linkage findings highlighting the 5p13 region and the frequency of
comorbid TS with ADHD, Laurin and colleagues (2009) also tested three associated ADHD
variants (rs2269272, rs3776581 and rs2032893) for association with TS. This study failed to
yield evidence of association. In an effort to identify sequence variants that may be involved
in altering SLC1A3 function, another study conducted by Adamczyk and colleagues (2011)
screened the coding region of 256 TS-affected individuals and 224 unaffected controls.
Despite finding a missense variant that could potentially alter the function of this protein,
allele frequencies were not significantly different between individuals with TS and
31
unaffected controls (Adamczyk et al., 2011). Other than these two studies, no additional
investigations have been conducted assessing the candidacy of SLC1A3 in TS.
1.12.3 Rationale for investigating SLC1A3 in TS and ADHD
As described earlier, the relationship between TS and ADHD is complex and not well
understood. Familial studies of TS patients have identified a subgroup of individuals affected
with TS and comorbid ADHD and OCD (TS+ADHD+OCD). This phenotype may represent
a distinct entity with unique genetic factors. Alternatively, it may represent a more
homogeneous subgroup of TS with additive and overlapping risk alleles from separate
disorders ADHD and OCD. Linkage studies reveal common susceptibility loci for TS and
ADHD, which may guide the exploration for genes conferring susceptibility to the
TS+ADHD+OCD trait.
Our selection of genes involved in the glutamatergic system was based on multiple
lines of evidence from studies of TS, OCD and ADHD (refer to The Glutamatergic
Hypothesis section). Although previous investigation of SLC1A3 by our lab did not find
association of SLC1A3 with TS (Laurin et al., 2009), only three SNPs were tested in a gene
spanning ~82 kilobases.
There is mounting evidence that dysfunction to this gene influences synaptic glutamate
levels (reviewed in Maragakis & Rothstein, 2004). GLAST-null rodents have demonstrated
alterations in motor coordination (Watase et al., 1998) and locomotive hyperactivity
(Karlsson, Tanaka, Heilig, & Holmes, 2008) suggesting that dysfunction of this gene directly
or indirectly influences motor function. Aberrant glutamate uptake has also been observed in
32
an accepted rat model of ADHD (Miller et al., 2014). Finally, expression patterns of EAAT1
mRNA coincide with postnatal CNS development events including neuronal differentiation
(Sutherland, Delaney, & Noebels, 1996). From a functional and positional standpoint,
SLC1A3 is a strong candidate for study in TS and ADHD.
1.13 Chromatin Signatures and Gene Expression
Identification of robust candidates for study based on gene function or position is merely the
beginning of uncovering the underlying etiology of a disorder. Following the selection of a
candidate gene, genetic association studies can be used to determine whether specific
changes to DNA sequences are associated with a particular trait. One way to begin
addressing this is by identifying variants located within functional regions of the genome.
Historically, variants located within the protein-coding regions of DNA were investigated for
a causative role in pathogenesis; however screening of protein-coding regions of associated
genes have not always clarify the mechanisms underlying complex genetic disorders
(Veenstra-VanderWeele, et al., 2001; Adamczyk et al., 2011). It is now known that only
1.5% of the human genome accounts for protein-coding sequences, with the majority of the
genome (an estimated 80.4%) participating in at least one regulatory event (The ENCODE
Project Consortium, 2012). The results of multiple GWAS studies provide suggestive
evidence that DNA changes to regulatory elements can result in disease (Hindorff et al.,
2009). Given these findings, the regulation of gene expression, or gene regulation, has been
proposed to play a larger role in pathogenesis than previously hypothesized (Ernst & Kellis,
2010; Hindorff et al., 2009).
33
Gene expression can be regulated at different levels. At the transcriptional level (in
eukaryotes), transcription is initiated by RNA polymerase binding upstream of the gene at
the promoter region. Transcription factors (TF) bind transcription factor binding sites (TFBS)
and form complex interactions with RNA polymerase to influence the expression of the gene.
TFBS can be located within the gene, upstream or downstream, as well as megabases from
the gene they regulate (Bulger & Groudine, 2011; Heintzman et al., 2007; Nobrega,
Ovcharenko, Afzal, & Rubin, 2003). Alterations to DNA sequences where TFBS are located
can increase (enhance) or decrease (repress) the affinity of the existing TF to its binding site
(Wang, Tomso, Liu, & Bell, 2005). Variation may also abolish the original TFBS or produce
a binding site for another TF (Wang et al., 2005).
In its default state DNA is tightly bound to its associated proteins, forming a complex
referred to as chromatin (Phillips & Hoopes, 2008). A unit of chromatin is a nucleosome,
which consists of 146 bp of DNA around four pairs of histone proteins (H2A, H2B, H3, H4).
When tightly bound to the histone proteins, transcription factors assess to DNA is restricted
and chromatin is considered closed; however, chromatin state is dynamic (Phillips & Hoopes,
2008). Specific chemical modifications to particular histone tails alter chromatin state via
chromatin remodeling (Formosa, 2003; reviewed in Kouzarides, 2007; Strahl & Allis, 2000).
The restructuring of nucleosomes using ATP-dependent complexes is what ultimately
influences TF access to DNA.
Considering that the many of GWAS associations map to sequences of non-coding
regions, variants located within regulatory regions are proposed to be involved in the
34
pathogenesis of many disorders (Hindorff et al., 2009). As combinations of histone
modifications patterns, or histone codes, are useful to detect cis-regulatory elements (Ernst &
Kellis, 2010; Ernst et al., 2011), multiple investigations, including the encyclopedia of DNA
elements (ENCODE), have attempted to map histone codes across the human genome.
Methylation and acetylation of lysine residues of histone tails are believed to play an
important role in chromatin state. For instance, open chromatin with enriched mono-
methylation of lysine 4 of the H3 histone protein (H3K4Me) is suggestive of enhancer
elements, regions where TF bind to facilitate transcription (Spicuglia & Vanhille, 2012; Ernst
et al., 2011). Another modification, acetylation of lysine 27 of the H3 histone protein
(H3K27Ac) is generally an indicator of active regulatory elements (Spicuglia & Vanhille,
2012; Ernst et al., 2011). In contrast, tri-methylation of lysine 4 of the H3 histone protein
(H3K4Me3) is a modification associated with active and poised promoter elements
(Spicuglia & Vanhille, 2012).
Although both histone methylation and acetylation have been associated with
regulatory regions, the mechanisms facilitating these chemical modifications differ. Histone
acetylation of lysine residues is mediated by histone acetyl-transferase (HAT) activity.
Acetylation of lysine residues facilitates transcription by neutralizing the electrostatic
interaction between positively charged histones and negatively charged DNA backbone
(reviewed in Struhl & Moqtaderi, 1998). This improves access of transcription factors to
binding sites. Binding of enzymes with HAT activity is therefore generally indicative of an
open chromatin state (Spicuglia & Vanhille, 2012). In contrast to histone acetylation, the
35
effect of histone methylation depends on the modified lysine residue, and can either facilitate
or repress transcription (Barski et al., 2007).
Regulatory variation not only plays an important role in genetic susceptibility, but
also phenotypic presentation and may account for the variability of the TS trait.
Investigations of complex genetic disorders such as TS and ADHD can use predictors of
regulatory elements (e.g., histone marks) to highlight noncoding regulatory regions of
candidate genes. This systematic approach to detecting potential causal variants may improve
efforts to clarify the mechanisms underlying disease and is used for this study.
36
Table 1–1. D
SM-V
Overview
of TS, O
CD
& A
DH
D
Tourette
OC
D
AD
HD
C
haracteristics m
otor and vocal tics obsessions and/or com
pulsions that interferes w
ith academic/occupational,
social, or functional activities
Inattentive and/or hyperactivity/impulsivity
that is inconsistent with developm
ental level and negatively im
pacts development or
functioning in more than one settings
Not due to drugs or other m
edical condition, and not better explained by other medical conditions
Duration
> 1 year > 1 hr/ day or cause
distress/impairm
ent in functioning > 6 m
onths
Onset
<18 years of age Early onset: ≤18 years Late onset: >18 years
< 12 years
Course
Wax and w
ane Increased severity ~10-
12years reduced severity into
adolescence/adulthood
Wax and w
ane Early onset: reduction of sym
ptoms by
adulthood in 40% of affected
individuals
Without treatm
ent improvem
ent of symptom
s is rare
Hyperactivity m
ay diminish in adulthood, but
inattention and impulsivity typically rem
ain
Estim
ated Prevalence 1%
2.5%
1 5%
Gender B
ias 4 m
ale: 1 female
Early onset > in m
ales 2 m
ales: 1 females
Review
ed in APA
(2013)
1Ruscio et al. 2010
37
Table 1–2. Sum
mary of L
inkage Studies of TS
Im
plicated Region (hg19)
Sample size
Measure
Score Study
Parametric
3q 171 fam
ilies LO
D
2.55 V
erkerk et al. (2006) 5p13.1-q11.2
12 families
LOD
3.05
Laurin et al. (2009) 1p, 3p
1 family
LOD
> 3
Knight et al. (2010)
11q23 1 fam
ily LO
D
3.24 M
érette et al. (2000) 14q31
1 family
LOD
2.4
Breedveld et al. (2010)
N
onparametric
5p15, 7q36, 11p13, 13q34, 14q13, 16p12, 19p13
7 large multigenerational
families
p-value <.00005
Barr (1999)
4q, 8p 110 A
SP M
LOD
> 2
TSAIC
G (1999)
4q, 5q, 17q 77 A
SP p-value
< .0001 Zhang et al. (2002)
5q34 1 fam
ily M
LOD
2.9
Curtis et al. (2004)
13q13 2.5
17q25 4 fam
ilies N
PL 2.61
Paschou et al. (2004) 2p32
304 ASP + 18 fam
ilies -log P
4.42
TSAIC
G (2007)
2p, 3p, 3q, 4p, 6p, 10p, 15p, 21p 304 A
SP -log P
≥ 2
2p, 5p, 6p 18 fam
ilies -log P
> 2 A
SP: Affected sib-pair
MLO
D: m
ultipoint maxim
um LO
D score
N
PL: nonparametric LO
D score
38
1.14 Research Aims & Hypotheses
For this study, two genes, SLC1A1 and SLC1A3, were investigated.
Study I: A Family-based Association Study of the putative Obsessive-Compulsive
Disorder gene, SLC1A1, with Tourette syndrome and Attention-
Deficit/Hyperactivity Disorder
Objective: The objective of the SLC1A1 study was to determine whether SLC1A1 is
associated with TS and/or ADHD.
Hypothesis: Given its previous association with OCD, as well as the involvement of this
gene in the glutamatergic system, SLC1A1 is a risk gene for TS and ADHD.
Study II: A Family-based Association Study of a Putative Attention-
Deficit/Hyperactivity Disorder gene, SLC1A3, with Tourette syndrome
Objective: The objective of the SLC1A3 study was to determine whether SLC1A3 is
associated with TS.
Hypothesis: Given previous published associations of this gene in ADHD, and the
overlapping linkage findings of ADHD and TS, SLC1A3 is a risk gene for TS.
39
Materials and Methods Chapter 2
2.1 The TS family-based sample
Approval for this protocol was obtained from the research ethics committee from the
Hospital of Sick Children Toronto and the University Health Network. Parental consent
and child asset were obtained in written form for children below 18 years of age. Signed
consent was obtained for individuals 18 years of age and older.
2.1.1 Recruitment and exclusion criteria
Participants with TS and their families were recruited from Ontario, Canada—with the
majority of the sample coming from the Greater Toronto Area. To qualify for this study,
probands were required to meet the Diagnostic and Statistical Manual of Mental
Disorders (3rd ed.; DSM-III-R; American Psychiatric Association, 1987) for TS or CMT.
Children could not have an additional diagnosis of autism, bipolar disorder or psychosis.
Children with a diagnosis of intellectual disabilities or pervasive developmental disorder
were also excluded from this study. Children with ADHD, OCD and/or OCB were not
excluded from this study.
2.1.2 Sample Composition
Our sample consisted of 303 nuclear families with at least one TS or CMT affected child
and 74 affected siblings (total of 377 affected children). The gender composition of our
sample consisted of 81% male and 19% female. The ethnic composition was
predominantly of European Caucasian ancestry (92%). The remainder of our sample
described their ancestry as non-European (3%) and 5% mixed European and Non-
40
European. In total 37% of the affected children also met the criteria for comorbid ADHD,
10% for comorbid OCD and 21% comorbid ADHD and OCD.
2.1.3 Diagnostic Assessment
The initial assessment consisted of self- and family- instruments. Information regarding
symptoms of TS and OCD were obtained based on the Yale Global Tic Severity Scale
(Leckman et al., 1989), the symptom checklist and ordinal scales of the Yale-Brown
Obsessive-Compulsive Scale (Goodman et al., 1989). Information regarding other
psychopathologies was also obtained using the Structured Clinical Interview for DSM-
III-R (Spitzer, Williams, Gibbon, & First, 1992) for adults, or the Kiddie Schedule for
Affective Disorders and Schizophrenia (K-SADS, Chambers, et al., 1985; Kaufman,
Birmaher, Brent, Rao, & Ryan, 1995) for individuals below 18 years of age. To insure
accuracy and validity, the collected information was checked by an experienced
neuropsychiatrist and complemented by the direct examination of each subject using the
same scales. Affection status was determined by a diagnosis of TS or CMT. Some of the
additional information regarding other conditions was collected for future analysis of
comorbid conditions, including OCD and ADHD.
2.2 The ADHD family-based sample
Approval for all protocols for this sample was obtained from the research committee at
the Hospital for Sick Children Toronto. Written informed consent and/or assent were
obtained for all subjects and parental consent for individuals below 18 years of age.
41
2.2.1 Recruitment and exclusion criteria
Participants (ages 7-16) and their families were recruited from the Child Development
and Neuropsychiatry outpatient clinics at the Hospital for Sick Children Toronto. To
qualify for this study, children were required to meet the DSM-IV criteria for one of three
ADHD-subtypes: predominately inattentive, predominantly hyperactive/impulsive, or
combined. Children with a score below 80 on both Performance and Verbal scales of the
Wechsler Intelligence Scale for Children (Wechsler, 1991) were excluded. The exclusion
criteria also included children with a neurological or chronic medical condition,
psychosis, bipolar affective disorder, TS or CMT.
2.2.2 Sample Composition
Our sample consisted of 641 individuals from 207 nuclear families with at least one child
affected with ADHD. Forty-seven siblings with ADHD were also included in our sample
for a total of 254 affected children. The combined subtype diagnosis was made for 61%
of affected children, 28% of children were diagnosed with the inattentive subtype and the
remaining 11% with the hyperactive/impulsive subtype. In regards to gender, the sample
comprised of 77% males and 23% female. The majority of individuals reported their
ethnicity as European or Caucasian (90%), while 10% described their ancestry as African,
Chinese, Indian, Native Canadian, or of mixed descent.
2.2.3 Diagnostic & Behavioural Assessment
For each child, information regarding behaviour and ADHD symptoms was obtained
using semi-structured interviews from both parents (Parent Interview for Child
Symptoms, PIC-IV, Ickowicz et al., 2006) and teachers (Teacher Telephone Interviews,
TTI-IV, Tannock et al., 2002). The PIC-IV provided information regarding additional
42
psychiatric conditions, behaviour at home, and other psychosocial stressors. While the
TTI-IV provided information regarding functioning at school and the child’s ability to
interact with others. This information was complemented with standardized instruments
including the Conners Parent and Teacher Rating Scales-R (Conners, 1997) and Ontario
Child Health Survey Scales-R (Boyle et al., 1993), which provided additional information
on ADHD symptoms, cognitive deficits and childhood experiences. These assessments
were taken following a medication-free period for at least 24 hours.
2.3 Isolation & Extraction of DNA
The DNA used for this study was isolated from white blood cells using a high-salt
extraction technique (Miller, Dykes, & Polesky, 1988).
2.4 Single-Nucleotide Polymorphism Selection
2.4.1 SLC1A1
SNP selection for the SLC1A1 study was based on two approaches. The first
approach involved selecting six SLC1A1 markers with previous association to OCD. The
second approach used data from the ENCODE project using the University of California,
Santa Cruz genome browser (http://genome.ucsc.edu/). H3K4Me1 and H3K27Ac marks
were used to indicate putative enhancer regions. Eleven SNPs located within putative
enhancer regions were selected and genotyped for study. Only markers with minor allele
frequencies greater than 5% were selected.
2.4.2 SLC1A3
For the SLC1A3 study, a total of ten SNPs were selected using three approaches.
The first approach involved retesting three markers (rs3776581, rs2269272, rs2032893)
43
previously examined by our lab (Laurin et al. 2009). Since the Laurin et al. (2009)
publications, our TS sample has expanded from 241 TS or CMT affected children to 377
TS or CMT affected children, giving the current study more power. The second approach
involved selecting the four strongest SLC1A3 tagSNP association signals from ADHD
GWAS (Neale, et al., 2010). Finally, the last approach involved the ENCODE project
using the University of California, Santa Cruz genome browser (http://genome.ucsc.edu/).
The latter two tools allowed us to identify enrichment of the H3K4Me1 and H3K27Ac
histone marks, indicators of putative enhancer regions. Enrichment of the H3K4Me3
histone mark was also used to identify the promoter region. Using this approach, three
markers located within these regions were selected and genotyped for study. Only
markers with minor allele frequencies greater than 5% were selected.
2.5 Single-Nucleotide Polymorphism Genotyping
For the global investigation, a total of 27 SNPs were genotyped: 17 SNPs in the SLC1A1
study (refer to Table 3-1 for SLC1A1 SNP list) and 10 SNPs in the SLC1A3 study (refer
to Table 4-1 for SLC1A3 SNP list). All genotyping was conducted using ABI 7900-HT
Sequence Detection System (Applied Biosystems, Foster City, CA) probes, which were
either available by assay-by-demand or requested upon submission of the DNA sequence
flanking the SNP-of-interest, by assay-by-design. TaqMan® 5’nuclease assay was used
for allelic discrimination for all assays.
Each assay consisted of sequence-specific primers that amplified the desired SNP
sequence. In addition, assays also contained two fluorescent-labeled probes used for the
detection of alleles present for the SNP-of-interest. The 3’ end of each probe consisted of
44
a quencher dye and 5’ fluorescent tag. Each probe is labeled with a different fluorescent
dye: VIC® dye was used to detect allele 1 and FAM® dye for allele 2.
Detection of fluorescence only occurred upon separation of the quencher from the
fluorescent dye. During the Polymerase Chain Reaction (PCR), which is responsible for
amplification of DNA, each probe hybridizes with the complementary DNA sequence.
AmpliTaq Gold® polymerase with nuclease activity separates the probe fluorescents
from the quencher. The fluorescence emitted depends on the allele present at the SNP of
interest. Data was collected for each sample using the ABI 7900HT Sequence Detection
System (SDS) with allelic discrimination mode of the SDS software package, v2.0
(Applied Biosystems, Foster City, CA).
The PCR reaction used for this study consisted of 30 ng of genomic DNA, 2.5 µl
of TaqMan® PCR Mix (Applied Biosystems) and 0.05 µl of allelic discrimination mix
(Applied Biosystems) with 36 µM and 8 µM of each primer and probe, respectively. The
total reaction volume was 5 µl. Ninety-six well optical reaction plates were used for
genotyping with two negative controls.
The PCR thermal cycling stages were as follows: i) 50°C for 2 minutes, ii) 95°C
for 10 minutes, iii) 40 cycles of: 94°C for 15 seconds of denaturing, and 59°C for 1
minute for annealing and extending.
2.6 Statistical Analysis
2.6.1 Association Analyses
Genetic association studies are used to determine whether a genetic marker is
associated with a particular disease or trait within a population. One approach to
45
determine association is to evaluate the differences of allele frequencies between affected
(cases) and unaffected individuals (controls) (reviewed in Lewis & Knight, 2012).
Employing this method, however, can result in an increase in type I error due to
population stratification, in which the allele frequencies between different ethnic groups
differ. Although the selection of controls from the same ethnic population as cases often
reduces type I error in case-control studies, spurious associations due to population
stratification remains a concern.
Another approach to identify genetic associations is to use a family-based design.
The Transmission Disequilibrium Test (TDT) is a modified χ2 test that controls for
population stratification by evaluating the distribution of allele transmission from
heterozygous parents to affected offspring (Sham & Curtis, 1995). Under the null
hypothesis of independent assortment, transmission of each allele from a heterozygous
parent to offspring should be 50%. Significant deviation in transmission of an allele
suggests that this particular allele is associated with the disorder or trait of interest. Using
an extended TDT for multi-allelic markers, each SNP of this investigation was tested for
single-marker association with TS or ADHD in the respective samples.
Alleles that are on the same chromosome that are in proximity to one another are
less likely to undergo recombination, and therefore more likely to be inherited as a set
(reviewed in Clayton, 1999). This set of alleles is referred to as a haplotype. The
TRANSMIT program uses a modified TDT to analyze extended marker haplotypes
(Clayton & Jones, 1999). For this study, the TRANSMIT program v2.5.4 (Clayton &
Jones, 1999) was used to identify any haplotype patterns that were significantly
transmitted to affected offspring. Transmission to affected offspring that is greater than
46
expect by chance suggested an association of specific haplotypes with TS or ADHD. To
detect a haplotype-association when frequencies were low, haplotypes with frequencies
less than 10% were pooled and only results with frequencies greater than 10% are shown.
2.6.2 Visualization and Interpretation of LD blocks
Neighbouring alleles that are transmitted together reflect haplotypes. Sites were
recombination frequently occurs over generations reflect divisions of haplotypes into
distinct LD blocks (reviewed in Daly, Rioux, Schaffner, Hudson, & Lander, 2001).
Visualization of LD blocks allows for the summarization of marker relationships and
makes it easier to evaluate which alleles belong to the same haplotype.
For this investigation, Haploview v4.2 software was employed to i) derive LD
pair-wise measures and ii) identify LD blocks. Genotype data loaded into the Haploview
software either came from our family-based samples or from the Human Haplotype Map
(HapMap) project, an international collaboration involved in identifying LD patterns and
marker allele frequencies of the human genome (International HapMap Consortium,
2003; www.hapmap.org).
LD blocks were defined using the Solid Spine method. This method partitions
blocks, such that the first and last markers in a block are in strong LD, although strong
LD between intermediate markers within a block is not a requirement.
r2 colour scheme was used to represent the LD relationships between SNPs. The
following characterizes the colour scheme and the LD relationship it highlights: i) r2 = 0
was represented by white, ii) 0 < r2 < 1 by shades of gray, and iii) r2 = 1 by black (Barrett,
Fry, Maller, & Daly, 2005).
47
2.6.3 Quality Control
The Merlin software v1.1.2 (Abecasis, Cherny, Cookson, & Cardon, 2002) was used to
highlight unlikely genotypes given gene flow patterns and recombination (Abecasis et al.,
2002). The Haploview program (v4.2, Barrett et al., 2005) was also used to check for
Mendelian errors. DNA assays that were highlighted by these programs for genotyping
errors were retyped. DNA samples that often produced inconsistent results were removed
from the study.
To ensure that our genotype frequencies did not significantly deviate from the
expected frequencies, conformance with Hardy-Weinberg equilibrium (HWE) was
checked using the Haploview program (v4.2, Barrett et al., 2005) for each SNP. Common
sources contributing to violations of HWE include: i) nonspecific primer/probe, ii)
genotyping errors iii) inappropriate genotyping calls (Hosking, et al., 2004). Therefore,
deviations were addressed first by ensuring that the primer/probe were specific to the
SNP of interest. This was done by evaluating whether another common SNP was located
within the primer/probe sequences. Nonspecific sequences detect neighbouring alleles,
which can result in deviations to the genotype frequencies. Another possible explanation
for deviations from HWE is due to genotyping errors (Hosking et al., 2004). To address
this, undetermined genotypes and unlikely genotypes were retyped. Finally, we referred
back to the SDS program to ensure that no inappropriate calls were made. Failure to
conform to HWE warranted the removal of that marker from the study.
2.6.4 Power
The primary objective of a genetic association study is to detect associations between
genetic factors and a trait of interest. The likelihood of detecting this association,
48
assuming that a relationship exists between the variables, can be determined by
conducting a power analysis.
A power analysis is a statistical test of the null hypothesis. Under the null
hypothesis there is no association between the selected marker and phenotype of interest.
Conversely, the alternative hypothesis states there is a relationship between the genetic
factor and phenotype. Rejecting the null hypothesis when it is correct is committing type
I error (α) and is reflected as false positives. Another type of error is failing to reject the
null hypothesis when it is incorrect—this is referred to as type II error (β). Power (1-β) is
the probability of avoiding type II error.
Three factors influence the power of a study: i) sample size (N) ii) threshold of
significance (α), and iii) effect size. Increasing the number of samples in a study is one
method of strengthening the power of a study and reducing the likelihood of committing
type II error. Adjusting the threshold of significance to make it more conservative may
also improve the likelihood that the detected associations reflect true positive findings.
Finally, effect size can also influence the strength of a study.
A measure that describes the effect size of a variable is the genotype relative risk
(GRR). GRR implies that the genotype of an individual is associated with risk of a
particular phenotype. For instance, if the GRR is less than 1, the genotype is considered
protective, as the individual is at a decreased risk affected with the phenotype of interest.
GRR equal to 1 suggests that there is no additional risk, and a GRR greater than 1
suggests that individuals with that particular genotype are at an increased risk. Risk
49
alleles with greater effect sizes are more likely to be detected by association studies
(Evans & Purcell, 2012).
Conducting a power analysis allows investigators to determine the smallest effect
at a given sample size as well as whether a study is underpowered. For this study, we
employed the Genetic Power Calculator (Purcell et al., 2003) was used to perform the
discrete trait TDT power analysis. Our TS sample consisted of 303 nuclear families with
74 siblings also met the diagnostic criteria for TS or CMT. As a result the power analysis
was calculated for a sample size of 377 parent-child trios. Based on epidemiology studies
of TS and CMT (Robertson, 2008; Scharf, Miller, Mathews, & Ben-Shlomo, 2012), the
disorder prevalence in the general population was specified as 1%.
For the SLC1A1 investigation using our TS/CMT sample, the threshold of
significance and adequate power were defined as 0.003 and 80%, respectively. Using
allele frequencies of 0.10-0.50 our study had adequate power to detect an estimated effect
size from 2.00-2.75 for a marker with a D’ of 1.0.
The power analysis for the SLC1A1 study using the ADHD sample was also
conducted using the Genetic Power Calculator (Purcell, 2003). Our ADHD sample
consisted of 207 nuclear families with 47 ADHD-affected siblings, for a total of 254
parent-child trios. The disorder prevalence used for this analysis was 5% (Polanczyk et al.,
2007) and the threshold of significance was set at 0.013. Using allele frequencies of 0.30-
0.50, our ADHD sample had adequate power (80%) to detect an estimated effect size
from 2.07-3.00 for a marker with a D’ of 1.0.
50
For the SLC1A3 study adequate power and the threshold of significance were set
to 80% and 0.006, respectively. Using our TS/CMT sample we would have had enough
power to detect an estimated effect size of 1.87-2.10 for allele frequencies of 0.20-0.40.
2.6.5 Correction for Multiple Testing
For this study, the Bonferroni correction was used to adjust the p values to account for
multiple testing. Due to the correlation between some of the SNPs, correcting for all
genotyped makers would have been overly conservative. To address this, the SNPSpD
tool (Nyholt, 2004) was used calculate the number of independent SNPs. SNPSpD uses
principal components analysis (PCA) (also referred to as eigenvalue analysis) to account
for the variance among a set of variables. The first eigenvalue accounts for the greatest
amount of variance between variables; the second eigenvalue, which is uncorrelated to
the first, accounts for the remaining variance (Ringnér, 2008). Each eigenvalue that
follows accounts for the next largest variance that was unaccounted for by prior principal
components (Ringnér, 2008). This method allows for the reduction of highly dimensional
data, so that it can be described using fewer variables (Price et al., 2006).
Eigenvalues may be used to measure the pairwise LD correlation between
markers from a correlation matrix (Cheverud, 2001). This correlation matrix is derived
using the input pedigree and map file (Nyholt, 2004). When the correlation between
markers is high, the first eigenvalue is also high as it accounts for most of the variance.
Further, when all of the SNPs are completely correlated the greatest possible variance of
the eigenvalues is equal to the number of SNPs in the LD matrix (Nyholt, 2004;
Cheverud, 2001). When there is no correlation among a set of SNPs in the matrix, all of
the eigenvalues equal 1 (Nyholt, 2004; Cheverud, 2001). In this case, the SNPs are
51
independent and the collective variance of eigenvalues is equal to zero (Nyholt, 2004;
Cheverud, 2001). This collective variance is employed to calculate number of
independent SNPs, which was then used to adjust the threshold for statistical significance.
The p values were corrected for multiple testing using the number of independent
SNPs as calculated by SNPSpD (Nyholt, 2004). For the SLC1A1 and SLC1A3 studies
the adjusted threshold of significance was 0.003 (p =0.05/15.029= 0.003) and 0.006 (p =
0.05/8.361= 0.006), respectively.
52
A family-based association study of the putative Chapter 3
Obsessive-Compulsive Disorder gene, SLC1A1, with
Tourette Syndrome and Attention-Deficit/ Hyperactivity
Disorder
3.1 Introduction
Gilles de la Tourette Syndrome (TS) is a childhood-onset neurobehavioral
disorder characterized by the presence of both motor and phonic tics for at least a year
(American Psychiatric Association, 2013). Family and twin studies provide evidence that
genetics play a substantial role in the pathogenesis of this disorder (Kidd et al., 1980;
Hyde et al., 1992; Pauls et al., 1981; Price et al., 1985; Walkup et al., 1988, 1996);
however, identification of risk genes with major effect remains elusive. Due to the
phenotypic variability of TS, it has been proposed that TS may be genetically
heterogeneous, with different phenotypes having unique and overlapping genetic risk
factors (Hyde et al., 1992; Mathews et al., 2006; Price et al., 1985). Under this
assumption, consideration of clinical phenotypes of TS may clarify genetic signals to date.
Chronic multiple tics (CMT) is a psychiatric disorder characterized by the
presence of either motor or vocal tics for at least a year (APA, 2013). This disorder is
commonly found in relatives of TS patients (Kidd et al., 1980; Pauls et al., 1981, 1984).
Twin studies of TS show that the concordance rate for monozygotic twins increases from
53% to 77%, with the inclusion of CMT subjects, adding to the support that chronic
multiple tics (CMT) represents a less severe manifestation of TS (Price et al., 1985).
53
Given that approximately 90% of TS-affected individuals have at least one
comorbid disorder (Robertson, 2012) phenotypic variability of TS may not be limited to
CMT, such that other disorders may also share risk genes with TS. Two disorders that are
commonly comorbid with TS are attention-deficit/hyperactivity disorder (ADHD) and
obsessive-compulsive disorder (OCD). ADHD is characterized by inappropriate
(inattentive, hyperactive/ impulsive, or a combination) behavior for a particular
developmental age (APA, 2013). This disorder has an estimated prevalence of 5%
(Polanczyk et al., 2007) and negatively impacts the individual’s quality of social and
academic/occupational functioning (APA, 2013). OCD affects an estimated 2.5% of
individuals (Ruscio et al., 2010) and is characterized by obsessions and/or repetitive
compulsions that result in significant distress or impairs functioning (APA, 2013). Both
ADHD and OCD are heritable (Biederman & Faraone, 2005; Hasler et al., 2007) and are
influenced by environmental factors and multiple genes. Together and individually these
disorders affect over 50% of the TS population (Freeman & Tourette Syndrome
International Database Consortium, 2007). On the basis that the prevalence of these
disorders is substantially higher in TS patients and their relatives than in the general
population, it is hypothesized that at least some subtypes of ADHD and OCD share
common risk factors with TS (Comings 1987, 2001; Pauls, et al., 1986, 1991, 1994;
Sheppard, Bradshaw, Purcell, & Pantelis, 1999)
In addition to high prevalence rates, multiple lines of evidence support an
etiological overlap between TS and OCD (Walkup et al., 1988; Mathews & Grados,
2011). Specifically, tic-related OCD, which describes cases of OCD were tics are also
present, has been observed in an estimated 40% of early-onset OCD patients (Grados et
54
al., 2001). Moreover, certain obsessive-compulsive symptom involving symmetry, order,
counting and touching are more likely to be found in tic-related OCD patients than those
without tics, suggesting that these OC symptoms may be etiologically related to the tic
disorder spectrum (Leckman et al., 1994-1995). Clinical studies provide further support
of this hypothesis by demonstrating that neuroleptics, a class of medication used to treat
tics, have greater therapeutic effect in treating tic-related OCD than selective-serotonin
reuptake inhibitors alone (reviewed in Singer et al., 2010; Pittenger, Krystal, & Coric,
2006). From a neuroanatomical perspective, many of the same brain regions (e.g.,
striatum) have been implicated in both disorders (Harrison et al., 2013; reviewed in Albin
& Mink, 2006). And from a heritability standpoint, studies have a significant genetic
correlation of around 41% between TS and OCD (Davis et al., 2013).
Due to the clinical heterogeneity among TS patients, it has been proposed that TS
may be separated into homogeneous subgroups, and that these subgroups may have
shared and unique susceptibility factors (Alsobrook & Pauls, 2002; Robertson & Cavanna,
2007; Grados et al., 2008). While comorbid TS and OCD has been identified as a
potential TS subgroup, another subgroup consisting of comorbid TS, OCD and ADHD
has also been identified (Grados et al., 2008). Considering that comorbid TS and ADHD
was not identified as a subgroup of TS from the Grados et al. (2008) study, it was
proposed that OCD may mediate the occurrence of comorbid TS and ADHD. In support
of this hypothesis, subsequent studies of these disorders provide support that OCD may
be mediating this heritable subtype. For instance, relatives of OCD/OCB probands have
been found to be at an increased risk for comorbid TS and ADHD (O'Rourke et al., 2011).
In addition, significant genetic correlations have been observed between TS and OCD
55
and for OCD and ADHD, but not for TS and ADHD (Mathews & Grados, 2011). The
results of these studies in the least suggest that TS, OCD and ADHD share some common
etiological factors.
One approach to identify genes conferring susceptibility to TS, and some forms of
ADHD and OCD is to investigate genes with biological relevance to all three disorders.
While dopaminergic and serotonergic genes have been the predominant focus in
candidate gene literature across these disorders, and likely contribute to pathogenesis,
additional evidence also suggests a role of the glutamatergic system. For instance,
transgenic mice with altered dopamine receptors of corticostriatal glutamate projections
exhibit tic and OC symptoms (McGrath, 2002; Nordstrom, 2002). In another study,
altered glutamate receptor activation was observed in spontaneous hypertensive rats
(Lehohla, Kellaway, & Russell, 2004), an accepted rodent model of ADHD (reviewed in
Sagvolden & Johansen, 2012). Elevated striatal glx levels have been observed in ADHD
patients through 1H-MRS, when compared to controls (Carrey N. J., MacMaster, Gaudet,
& Schmidt, 2007), and drugs interacting with the glutamatergic system are currently
being studied for the reduction of tic and obsessive-compulsive symptoms (Singer, 2010).
The role of the glutamatergic system in the pathogenesis of these disorders has been
suggested by neuroimaging studies of OCD (reviewed in MacMaster, 2008) and ADHD
(Acros-Burgos et al., 2012; Courvoisie, Hooper, Fine, Kwock, & Castillo, 2004;
Dramsdahl et al., 2011; Ferreira et al., 2009; MacMaster, Carrey, Sparkes, & Kusumakar,
2003; Perlov et al., 2010), as well as analysis of postmortem brain tissue from individuals
with TS (Anderson, 1992). Finally, genetic association studies implicate genes involved
in the glutamatergic system as possible contributors of TS (Crane, et al., 2011), OCD
56
(Arnold et al., 2004, 2006; Dickel et al., 2006; Stewart et al., 2007; Wendland et al.,
2009) and ADHD (Dorval et al., 2007; Turic et al., 2005; Laurin et al., 2008).
Despite multiple genome-wide linkage scans (e.g., The Tourette Syndrome
Association International Consortium for Genetics, 1999, 2007; Barr et al., 1999;
Breedveld, Fabbrini, Oostra, Berardelli, & Bonifati, 2010; Fisher et al., 2002; Ogdie et al.,
2003, 2004; Hebebrand et al., 2006; Hanna et al., 2002; Shugart et al., 2006; Samuels et
al., 2007) no susceptibility regions have been linked to all three disorders. Recent
genome-wide association studies (GWAS) in TS (Scharf et al., 2012), OCD (Stewart et
al., 2013) and ADHD (Neale et al., 2008, 2010; reviewed in Poelmans et al., 2011), have
also been conducted to identify common genetic variants; however, due to a lack of
power, few variants have met the threshold of genome-wide significance (p = 5 x 10-8).
Nevertheless, association signals from GWAS and targeted association studies may be
used to identify risk genes.
Multiple candidate gene association studies of OCD have reported association
with solute carrier family 1, member 1 (SLC1A1), a gene that encodes for the excitatory
amino acid transporter (EAAT3; Arnold et al., 2006; Dickel et al., 2006; Stewart et al.,
2007; Wendland et al., 2009; Shugart et al., 2009). While a meta-analysis of SLC1A1 did
not provide strong evidence for association of this gene with OCD (Stewart et al., 2013),
this protein is involved in the removal of glutamate from the synaptic clef and is relevant
to the regulation of glutamate in the brain (Tzingounis & Wadiche, 2007). Given the
evidence implicating SLC1A1 in OCD, as well as its biological relevance to TS and
ADHD, it is surprising that there are no published candidate gene studies investigating
the contribution of SLC1A1 to TS and ADHD.
57
Despite multiple association reports with OCD, SLC1A1 associated variants have
differed between studies and no non-synonymous DNA changes in the coding region
have been found that could explain the potential mechanisms underpinning risk
(Veenstra-VanderWeele et al., 2001, 2012; Wang et al., 2009). Recent evidence suggests
that genetic variation influencing the regulation of gene expression may also contribute to
the susceptibility of complex genetic disorders (Siniscalco, Cirillo, Bradstreet, &
Antonucci, 2013); therefore, we also sought to identify functional DNA changes that may
account for any possible associations.
In this study, we first sought to determine whether SLC1A1 contributes to TS by:
(1) testing associated OCD single-nucleotide polymorphisms (SNPs) and (2) testing
SNPs within putative enhancer regions using our TS sample which consisted of DNA
from 303 nuclear families and 74 affected siblings. We then took our four most positive
markers for study using our ADHD sample, which consisted of DNA from 207 nuclear
families and 47 affected siblings. In total 17 SNPs were genotyped and tested for
association using our TS sample and 4 were tested for association in our ADHD sample.
58
3.2 SLC1A1 Results
Single-marker analysis
Six polymorphisms that produced association, or trends for association using
OCD samples (rs3933331, rs7858819, rs301430, rs301434, rs301435, rs3087879) were
initially genotyped in our TS sample. Transmission Disequilibrium Test (TDT) analysis
was used to identify biased transmission of alleles from heterozygous parents to affected
children. We observed biased transmission of the rs301430 C allele to affected TS
children (p = 0.014). Although this finding would not withstand correction for multiple
testing biased transmission of this allele has also been reported to individuals with early-
onset OCD (Dickel et al., 2006). Interestingly, the effect of the rs301430 C allele has
been shown increased reporter gene expression when compared to the T allele (Wendland
et al., 2009).
Given the number of previous associations between this gene and OCD, as well as
our trend in TS, we sought to identify functional markers to explain the trends and
improve power. None of the common variants located within this gene are non-
synonymous amino acid changes, therefore we focused on variants located within
putative regulatory elements. Using H3K4Me1 and H3K27Ac marks, which are
indicative of enhancer regions, we selected eleven SNPs with minor allele frequencies
greater than 5%, and tested them for association with our TS sample (see Figure 3-2 for
relative position of genotyped SLC1A1 markers). Subsequent single-marker analysis of
these polymorphisms provided no evidence of association with our TS sample (Table 3-
1).
59
Four of our most positive markers in TS (based on the single- and haplotype-
analysis results) were also genotyped and tested for association in our ADHD sample. We
observed biased transmission of rs301435 T allele to affected ADHD children (p = 0.006).
This association withstands the correction for multiple testing for the markers genotyped
in the ADHD sample in this study alone.
Haplotype Analysis
The TRANSMIT program (Clayton & Jones, 1999) was used to detect haplotype
association of LD blocks. Using the Solid Spine of LD, in which strongest LD is found
between the first and last markers of a block, 3 blocks were constructed using the TS
sample. While no evidence of association for the first two blocks was found (Blocks 1 &
2 results not shown), we did observe a trend for a three-marker haplotype
(rs301434/rs301435/rs3087879) in block 3 (global χ2 = 20.761, 7 df, global uncorrected p
= 0.004). The TRANSMIT program was also used to detect haplotype association of LD
blocks identified in the ADHD sample. Only one block was constructed using this sample,
which consisted of the same three-marker haplotype (rs301434/rs301435/rs3087879)
found using our TS sample. Despite observing a trend in TS, this haplotype did not reach
the threshold of significance in ADHD (Table 3-4 for results).
60
3.3 Tables & Figures
Table 3–1. SLC1A1 Single SNP analysis for TS sample
Marker Position MAF
(Allele)
HW p-value Transmission Ratio
(Allele)
χ2 p-value
rs3933331 4389941 0.186 (C) 0.653 87:69 (G:C) 2.077 0.150
rs10491731 4542803 0.140 (C) 0.862 54:70 (A:C) 2.065 0.151
rs4641119 4544907 0.290 (C) 0.264 92:101 (A:C) 0.420 0.517
rs7858819 4559892 0.230 (T) 0.983 93:108 (C:T) 1.119 0.290
rs7858877 4560569 0.465 (C) 0.925 123:126 (T:C) 0.036 0.849
rs3780413 4567353 0.314 (C) 0.460 110:98 (G:C) 0.692 0.405
rs4740790 4568342 0.310 (A) 0.313 106:90 (G:A) 1.306 0.253
rs7871243 4573218 0.461 (A) 0.732 123:125 (G:A) 0.016 0.899
rs12682807 4574022 0.089 (C) 0.014 49:54 (A:C) 0.243 0.622
rs10117931 4575120 0.464 (A) 0.882 129:134 (G:A) 0.095 0.758
rs301430 4576680 0.280 (C) 0.909 90:126 (T:C) 6.000 0.014
rs301434 4582082 0.473 (T) 0.896 138:125 (C:T) 0.643 0.423
rs301435 4582843 0.468 (C) 0.734 144:139 (T:C) 0.088 0.766
rs3087879 4586808 0.343 (C) 0.933 136:113 (G:C) 2.124 0.145
rs301443 4594919 0.293 (G) 0.091 114:114 (C:G) 0.000 1.000
rs10974636 4596213 0.285 (T) 0.448 99:103 (G:T) 0.079 0.778
rs34345944 4596315 0.173 (G) 0.192 66:81 (A:G) 1.531 0.216
61
Table 3–2. SLC1A1 Haplotype analysis of Block 3 for TS sample
rs301434 rs301435 rs3087879 Frequency Observed Expected Var
(O-E)
χ2 (1df) p-value
1 (C) 2 (T) 2 (G) 0.522 381.230 370.700 80.254 1.382 0.240
2 (T) 1 (C) 1 (C) 0.332 226.260 237.480 68.927 1.828 0.176
2 (T) 1 (C) 2 (G) 0.124 98.398 90.671 35.205 1.696 0.193
Global χ2 test (7 df) with frequencies > 10% = 20.761, uncorrected p = 0.004
62
Table 3–3. SLC1A1 Single SNP analysis for ADHD sample
Marker Position MAF
(Allele)
HW p-value Transmission Ratio
(Allele)
χ2 p-value
rs301430 4576680 0.302 (C) 0.380 70:67 (T:C) 0.066 0.798
rs301434 4582082 0.473 (T) 0.248 107:83 (T:C) 3.032 0.082
rs301435 4582843 0.476 (C) 0.096 115:77 (C:T) 7.521 0.006
rs3087879 4586808 0.333 (C) 0.659 100:79 (C:G) 2.464 0.117
Table 3–4. SLC1A1 Haplotype analysis for ADHD sample
rs301434 rs301435 rs3087879 Frequency Observed Expected Var
(O-E)
χ2
(1df)
p-value
1 (C) 2 (T) 2 (G) 0.505 306.930 314.200 61.278 0.862 0.353
2 (T) 1 (C) 1 (C) 0.308 195.470 191.870 56.830 0.229 0.633
2 (T) 1 (C) 2 (G) 0.146 99.960 91.091 32.101 2.450 0.118
Global χ2 test (7 df) = 13.165 with frequencies > 10%, p = 0.068
63
Figure 3-1. LD plot of 17 genotyped SLC1A1 SNPs. The numbers within each block represents r2 and are indicative of LD between SNPs in the TS sample. LD blocks defined using the solid spine of LD are outlined in bold.
Figure 3-2. Relative positions of 17 genotyped SLC1A1 markers
64
A family-based association study of a putative Chapter 4
Attention-Deficit/Hyperactivity Disorder gene, SLC1A3,
with Tourette syndrome
4.1 Introduction
Gilles de la Tourette syndrome (TS) is a childhood-onset neurobehavioral
disorder characterized by the presence of both motor and phonic tics for at least a year
(American Psychiatry Association, 2013). Family studies of TS demonstrate that this
disorder is heritable, with both environmental and genetic factors playing a role in its
etiology (Kidd et al., 1980; Pauls et al., 1981; Walkup et al., 1988, 1996; Price et al.,
1985; Hyde et al., 1992). Despite this evidence, multiple association studies and the only
published genome-wide association study (GWAS) of TS have been unable to identify
many genes contributing to susceptibility (Scharf et al., 2012). One explanation for the
difficulty identifying risk genes is due to the heterogeneity of the TS phenotype. For
instance, multiple lines of evidence suggests that chronic multiple tics (CMT) is a less
severe phenotype of TS (Pauls et al., 1986, 1990, 1991; Walkup et al., 1996; Grados et al.,
2008). The presence of additional psychiatric disorders in the majority of TS probands,
including combinations of comorbid attention-deficit/ hyperactivity disorder (ADHD) or
obsessive-compulsive disorder (OCD), and subclinical OCD (OCB) suggest that TS is
heterogeneous and of complex etiology. Consideration of homogeneous TS subgroups by
genetic studies may improve efforts in identifying risk genes by clarifying whether these
manifestations represent TS subgroups with shared and additional risk factors.
65
ADHD is a psychiatric disorder characterized by inappropriate inattentive,
hyperactive/impulsive behaviour for a particular developmental age (American
Psychiatric Association, 2013). This disorder interferes with functioning or development
(APA, 2013) and affects approximately 5% of children worldwide (Biederman & Faraone,
2005). Clinical and epidemiological studies reveal that although TS affects approximately
1% of children worldwide (Robertson, 2012), an estimated 50% of these children also
meet the diagnostic criteria for ADHD (Freeman et al,. 2000, 2007).
Despite the high prevalence of ADHD with TS (TS+ADHD), the relationship
between these disorders is unclear (Comings & Comings, 1984; Pauls et al., 1986, 1991,
1993; Knell et al., 1993; Stewart et al., 2006; Grados et al., 2008; O’Rourke et al., 2011;
Mathews & Grados, 2011). For instance, Knell et al. (1993) observed a significantly
greater rate of ADHD among relatives of TS only and TS+ADHD probands, when
compared to relatives of unaffected individuals; however, the rates of ADHD between TS
only and TS+ADHD probands also significantly differed (Knell et al., 1993). In another
study the rate of TSonly was not significantly elevated amongst relatives of ADHDonly
probands, while the rate of TS+ADHD in relatives was significantly greater across all
probands (TS-only, ADHD-only, TS+ADHD) (Stewart et al., 2006). A plausible
explanation for these findings is that ADHD that is comorbid with TS may be a subtype
of TS, with unique and shared risk factors as ADHD without TS (Knell et al., 1993;
Rizzo et al., 2007; O’Rourke et al., 2011).
Multiple reports of linkage to several chromosomal regions support a genetic
overlap between these disorders, including: 4q, 5p, 8p, 10q, 11q, and 17p (Arcos-Burgos
et al., 2004; Barr et al. 1999; Curtis et al., 2004; Fisher et al., 2002; Hebebrand et al.,
66
2006; Laurin et al., 2008; Ogdie et al., 2006; Simonic, Gericke, Ott, & Weber, 1998;
TSAICG, 1999, 2007). Amongst the overlapping susceptibility loci, chromosomal region
5p12-13 has been consistently linked to ADHD through several genome-wide linkage
scans (Fisher et al., 2002; Ogdie, et al., 2003, 2004, 2006; Bakker et al., 2003; Hebebrand
et al., 2006) and suggestive evidence from two genome-wide linkage scans have also
implicated part of this region in TS (Barr et al., 1999; TSAICG, 2007). Although none of
the genetic variants mapping to 5p13 reached the threshold of genome-wide significance
in the recent GWAS of TS (Scharf et al., 2012) a more recent investigation of the
heritability of TS by chromosome demonstrated greater heritability loading for
chromosome 5 (Davis et al., 2013). Based on these findings, genes mapping to 5p13 with
biological relevance to both disorders are good candidates for study in the search for
shared risk genes between TS and ADHD.
Altered glutamate neurotransmission has been implicated in the pathogenesis of
TS and ADHD from multiple lines of study. For instance, proton magnetic resonance
studies have observed altered glx (glutamate and glutamine) levels in the frontal-striatal
regions of ADHD patients (Dramsdahl, 2011; MacMaster et al., 2003; Perlov, 2007,
2009) and altered glutamate levels have been reported using postmortem analysis of
brains of TS affected patients (Anderson et al., 1992). Proton magnetic resonance
spectroscopy studies have reported abnormal glx levels in the basal ganglia of ADHD
patients (Carrey et al., 2007; Maltezos et al., 2014) and new treatment options that
modulate glutamate release are being studied for a role in tic management (Singer, 2010).
Finally, disrupted glutamate transmission has also been observed in spontaneous
hypertensive rats (Warton, Howells, & Russell, 2009), an accepted model of ADHD
67
(Lehoha et al., 2004) as well as tic-like movements in rodent models of TS (McGrath et
al., 2000; Nordstrom, 2002). On the basis of this evidence, dysfunction to the
glutamatergic system may contribute to TS and ADHD. Therefore, genes involved the in
the glutamatergic system are biologically relevant to the pathogenesis of TS and ADHD.
Solute carrier family 1, member 3 (SLC1A3) maps to chromosome 5p13 and
encodes for a glial excitatory amino acid transporter (EAAT1). This transporter is
involved in the clearance of glutamate from the synaptic cleft and prevents glutamate-
induced exictoxicity (Kanai et al., 2013). To date, SLC1A3 has been implicated in two
family-based association studies of ADHD (Turic et al., 2005; Elia et al., 2009) and
produced association signals in a GWAS of ADHD (Neale et al., 2010).
Given the overlap in linkage findings, genes found within the 5p13 region that
have been implicated in ADHD are relevant to the genetics of TS. In 2009, our lab
conducted the first association study of SLC1A3 in TS using a sample consisting of 241
affected children (Laurin et al. 2009). Three ADHD associated single nucleotide
polymorphisms (SNPs) were genotyped in the TS sample. While no positive findings
with TS were observed, it would take 27 tagSNPs (r2 > 0.80) to adequately test whether
genetic variation to this gene is associated with TS. Since the Laurin et al. (2009) study, a
subsequent genetic study of SLC1A3 tested the only non-synonymous mutation, marker
rs2032892, for association with an independent TS sample of 256 affected individuals
and 224 unaffected controls; however, the result of this study did not reach the threshold
of significance (Adamczyk et al., 2011). Other than these investigations, no other studies
have been published exploring whether other SLC1A3 markers are associated with TS.
68
Since the publication by Laurin et al. (2009), an additional candidate gene study
has associated SLC1A3 with ADHD (Elia et al., 2009). In addition, GWAS have since
been conducted for both ADHD (Neale, et al., 2008, 2010; Polemans et al., 2011) and TS
(Scharf et al., 2012). Although no significant evidence of association for SLC1A3 has
been identified by these GWAS, few variants reached genome-wide significance (p = 5 x
10-8). Given that top signals from these studies may reflect true associations, follow-up
studies of these signals of SLC1A3 merit further investigation.
Prior to the GWAS era, studies of complex genetic disorders focused on the
impact of non-synonymous mutations of protein-coding region. Recent evidence suggests
that variation in non-coding sequences responsible for the regulation of gene expression,
may also play a role in the pathogenesis of complex genetic disorders (Hindorff et al.,
2009; Siniscalco et al., 2013) At the transcriptional level, the regulation of gene
expression is mediated through binding of transcription factors to transcription factor
binding sites located within regulatory regions (e.g., enhancer or promoter regions).
Given that many disease-associated SNPs identified by GWAS are located in or near non-
coding putative regulatory regions of the genome (Manolio, Brooks, Collins, 2008; The
ENCODE project consortium, 2012) alleles located within regulatory regions may have
functional implications on complex genetic disorders and merit further study.
To date, there is little evidence demonstrating that non-synonymous variants in
the coding region of SLC1A3 account for previous associations with ADHD. Despite this,
variants located within non-coding putative regulatory regions of SLC1A3 have not been
well studied. Similar to results from other GWAS (reviewed in Hindorff et al., 2009), the
strongest GWAS findings of ADHD and TS predominately consist of variants located
69
outside of protein-coding regions (e.g., intronic or intergenic SNPs) that have no known
function (Scharf et al., 2012; Neale, et al., 2008, 2010, reviewed in Poelmans et al., 2011).
It has been proposed that many of the top-signaling SNPs may localize to putative
regulatory regions (reviewed in Manolio, 2013; The ENCODE project consortium, 2012).
For this reason, predictions of regulatory regions may be used to interpret strong signals
from GWAS and improve the power of studies attempting to identify causal variants of
disease (Ernst et al., 2011; The ENCODE project consortium, 2012).
Based on the candidacy strength of SLC1A3, we conducted a family-based
association study using a TS sample of 303 nuclear families with at least one TS affected
child (for a total of 377 affected children). Our objective was to determine if SLC1A3, a
putative ADHD gene, was also associated to TS. SLC1A3 SNPs were selected using three
approaches: (1) re-evaluating three previously investigated SNPs for association using
our extended sample (from 241 affected children to 377 affected children), (2) testing the
strongest four SLC1A3 tagSNP signals from the ADHD GWAS (Neale, et al., 2008,
2010), and (3) testing three markers localized to putative regulatory regions that may help
interpret previous association findings. In total ten SLC1A3 markers were genotyped and
tested for association in our TS sample.
4.2 SLC1A3 Results
Single-marker analysis
To determine whether SLC1A3, an associated ADHD gene, is also associated with
TS we selected seven associated ADHD SLC1A3 markers based on previous reports.
Three of the seven markers (rs2269272, rs3776581 and rs2032893) were selected from a
70
candidate gene association study (Turic et al., 2005) and four tagSNPs (rs4354072,
rs2562571, rs2303716, rs1529461) were selected from GWAS of ADHD (Neale et al.,
2010). Each marker was tested for association in our TS sample, which consisted of 303
nuclear families with at least one TS or CMT affected child (a total of 377 affected
children). Transmission Disequilibrium Test (TDT) analysis, a test for biased
transmission of alleles from heterozygous parents to affected offspring, was used to
determine whether the marker alleles were associated with TS.
Similar to published results from our lab (Laurin et al., 2009), we were unable to
find an association between three ADHD associated SNPs (rs2269272, rs3776581,
rs2032893; Turic et al., 2005) and TS using our sample. Biased transmission of the
rs2562571 T allele to affected TS children (p = 0.025) was observed, although this
finding would not withstand correction for multiple testing. This marker was originally
selected from a meta-analysis of ADHD GWAS (Neale et al., 2010) and is located
upstream of SLC1A3 in the intergenic region. To determine the potential mechanism of
this variant, we used histone markers that were informative of enhancer regions (see
Methods for further detail). We found no evidence that marker rs2562571 maps to a
putative regulatory region nor were we able to identify any common SNPs in LD (r2
> .80) with this marker that localize within a regulatory region, using HapMap data.
Although no SNPs tagged marker rs2562571 (r2 > 0.80), using HapMap data
marker rs2731901 showed the strongest correlation (r2 > 0.50) and mapped to a predicted
enhancer region. Based on the proximity of the rs2562571 association signal to the
promoter region, we also selected two additional SNPs (rs4443439 and rs2869675) for
association testing that overlapped the promoter region of SLC1A3. Testing of these
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additional SNPs using the TDT analysis failed to meet the threshold of significance or
demonstrate any trends (see Table 4-1).
Haplotype analysis
In addition to single-marker analysis, we also conducted haplotype analysis for
two LD blocks using the TRANSMIT program (Clayton & Jones, 1999). LD blocks were
defined using Solid Spine of LD, in which strongest LD can be found between first and
last markers of a block. While no significant evidence of haplotype association was found,
two haplotypes rs2562571/rs4869675 and rs2269272/rs1529461 showed trends were
observed with TS (p-values shown in Table 4-2 and 4-3, respectively; LD structure
shown in Figure 4-1). For the first haplotype, rs2562571/rs4869675, we observed an
overtransmission of the rs2562571 T and rs4869675 G haplotype (χ2 = 3.924, 1 df, p =
0.048; see Table 4-2), and an undertransmission of the rs2562571 C and rs4869675 T
haplotype (χ2 = 4.223, 1 df, p = 0.040; see Table 4-2) to affected offspring. For the
second haplotype, we found an undertransmission of the rs2269272 C and rs1529461 A
haplotype to affected TS offspring (χ2 = 4.379, 1 df, p = 0.036; see Table 4-3). Although
haplotypes rs2562571/ rs4869675 and rs2269272/ rs1529461 showed trends in TS,
neither global association tests were significant (see Table 4-2 and Table 4-3, respectively
for global analysis results). Furthermore, these findings did not reach the threshold of
significance in TS, and therefore should be interpreted with caution.
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4.3 Tables & Figures
Table 4–1. Single-marker TDT analysis for 10 genotyped SLC1A3 SNPs in TS sample
Marker Position MAF
(Allele)
HWp Transmission
Ratio (Allele)
χ2 p-value
rs4354072 36367860 0.333 (T) 0.250 110:126 (C:T) 0.949 0.330
rs2731901 36574507 0.190 (G) 0.962 97:82 (A:G) 1.257 0.262
rs2562571 36597841 0.211 (C) 0.849 105:75 (T:C) 5.000 0.025
rs4869675 36600919 0.309 (G) 0.341 129:112 (G:T) 1.199 0.274
rs4443439
(rs4020423)
36607791 0.316 (T) 0.163 136:125 (C:T) 0.464 0.496
rs3776581 36661944 0.336 (A) 0.900 92:84 (G:A) 0.364 0.547
rs2303716 36680125 0.302 (G) 0.175 110:97 (A:G) 0.816 0.366
rs2269272 36687856 0.178 (T) 0.720 84:90 (C:T) 0.207 0.649
rs1529461 36689363 0.219 (A) 0.995 86:105 (A:G) 1.890 0.169
rs2032893 36698622 0.411 (T) 1.000 93:92 (C:T) 0.005 0.941
Table 4–2. SLC1A3 Haplotype analysis of rs2562571/rs4869675 for TS sample
rs2562571 rs4869675 Frequency Observed Expected Var
(O-E)
χ2
(1df)
p value
T T 0.488 336.600 337.830 78.812 0.019 0.890
T G 0.297 223.350 206.990 68.222 3.924 0.048
C T 0.203 131.740 146.450 51.293 4.223 0.040
Global χ2 (3df) with frequencies > 10% = 6.022, p = 0.111
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Table 4–3. SLC1A3 Haplotype analysis of rs2269272/rs1529461 for TS sample
rs2269272 rs1529461 Frequency Observed Expected Var
(O-E)
χ2
(1df)
p value
C G 0.606 452.090 442.340 80.603 1.179 0.278
C A 0.218 143.680 158.900 52.862 4.379 0.036
T G 0.175 130.170 124.180 47.853 0.750 0.387
Global χ2 (3df) with frequencies > 10% = 5.668, p = 0.129
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Figure 4-1. LD plot of 10 genotyped SLC1A3 SNPs. The numbers within each block represent r2 and are indicative of LD between SNPs. LD blocks were defined using the solid spine of LD are outlined in bold.
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Figure 4-2. Relative positions of the 10 genotyped SLC1A3 SNPs.
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Discussion & Future Directions Chapter 5
5.1 Discussion
In this investigation, we presented the first association study of SLC1A1 in TS and
ADHD, as well as a follow-up study of SLC1A3 in TS. Our TS sample consisted of DNA
from 377 TS or CMT affected children, and our ADHD sample consisted of DNA from
254 ADHD affected children. While we hypothesized that SLC1A1 and SLC1A3 confer
susceptibility to TS and ADHD, we were unable to detect significant associations using
our samples. However, we did observe trends, which may be indicative of true findings.
Given the role of these genes in the glutamatergic system, these loci merit further study in
larger samples.
5.1.1 SLC1A1
SLC1A1 is a gene that has been associated with OCD, a disorder that is commonly
comorbid with TS. Despite evidence implicating SLC1A1 as a risk gene of OCD, the
proposed genetic relationship of OCD, TS and ADHD, and its involvement in neuronal
glutamate clearance, there are no studies assessing SLC1A1 as a risk gene for TS or
ADHD. For this reason, we sought to determine whether SLC1A1 is also a common risk
gene for TS and ADHD through a family-based association study. Considering that the
genetic relationship between TS and ADHD may be mediated by OCD, we tested OCD
associated variants of SLC1A1 using our TS and ADHD samples.
For the single-marker analysis of SLC1A1 in TS, our strongest association signal
came from marker rs301430, a synonymous mutation within exon 10. This marker was
selected based on its association with early-onset OCD as a single-marker (Arnold et al.,
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2006) and due to its inclusion of several OCD-associated haplotypes (Dickel et al., 2006;
Stewart et al., 2009; Wendland et al., 2009). In the current investigation we also observed
weak evidence of association for a three-marker haplotype rs301434/rs301435/rs3087879
with the TS sample (global uncorrected p = 0.004). Interestingly, this corresponds to a
haplotype associated in an OCD study reported by Arnold and colleagues (2006).
However, our findings did not withstand correction for multiple tests.
In a study using tissue from postmortem brains, Wendland and colleagues (2009)
investigated whether SLC1A1 SNPs were associated with SLC1A1 mRNA expression
levels in the prefrontal cortex. The association signal of marker rs301430 was nominally
significant with SLC1A1 mRNA levels, with an increase in expression coinciding with
the number of C alleles (Wendland et al., 2009). The results of a follow-up in-vitro
analysis of SLC1A1 expression using a luciferase reporter gene assay suggested that
rs301430 might be a causal variant (Wendland et al., 2009). We were unable to find any
replication studies to refute or support these findings. However, inconsistent reports of
which rs301430 allele is associated with OCD brings doubt as to whether this variant is a
causal polymorphism. For instance, Dickel et al. (2006) observed overtransmission of the
rs301430 C allele to subjects with early-onset OCD. However, using an independent
OCD sample, Stewart et al. (2007) observed an overtransmission of the rs301430 T allele
to OCD probands. In the present study we observed an overtransmission of the rs301430
C allele to TS probands. One possible explanation for the association of opposite alleles
is that rs301430 is in LD with the causal variant (Lin, Vance, Pericak-Vance & Martin,
2007); however, other association studies of OCD and SLC1A1 were unable to detect an
association signal for this single-marker (Shugart et al., 2009; Samuels et al., 2011;
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Taylor et al., 2012; Wu et al., 2013), including a recent meta-analysis of SLC1A1 using
OCD subjects (Stewart et al., 2013). Alternatively, the lack of replicated associations for
this marker may be the result of sampling from differing ancestral populations or
phenotypes examined across studies (Lin et al., 2007; reviewed in Hemmings & Stein,
2006).
In light of our trends in TS for both single- and haplotype- analysis, we tested
these four markers (rs301430, rs301434, rs301435, rs3087879) for association with our
ADHD sample. In contrast to our TS results, we were unable to identify a significant
association between single-marker rs301430 and haplotype
rs301434/rs301435/rs3087879 with the ADHD sample. However, we did observe a weak
association for single-marker rs301435 with our ADHD sample (p = 0.006), but this
finding barely remained significant after multiple-testing corrections.
Despite observing trends for two markers relatively proximal to each other, the
two implicated SNPs were poorly correlated. Considering that the majority of SLC1A1
associations with OCD localize to non-coding regions of the human genome, changes to
DNA sequences of regulatory elements may underlie susceptibility to OCD and its
related disorders (e.g., TS and ADHD). In addition to testing OCD associated SNPs with
TS we also selected SNPs that localized to predicted enhancer elements. The premise
behind this methodology was based on recent findings of large association studies,
including GWAS, which indicated enrichment for SNPs within regulatory elements
(Edwards, Beesley, French, & Dunning, 2013; Schorck et al., 2013). SNPs that localize to
enhancer regions are more likely to be functionally relevant and provide a possible
explanation of the mechanisms underlying associated variants. Despite our efforts to
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narrow the search for functionally relevant alleles none of the additionally genotyped
SNPs reached the threshold of significance.
5.1.2 SLC1A3
The second part of this investigation was based on a common TS- and ADHD-
susceptibility region on chromosome 5p13. To date, several genetic association studies
have implicated this gene in ADHD. Yet comprehensive investigations of this gene in TS
are lacking. On the basis that SLC1A3 is a strong positional and functional candidate risk
gene for both ADHD and TS, we sought to determine whether previously reported
association findings for ADHD and three SLC1A3 SNPs (Turic et al., 2005) would be
associated with TS. While the results of our initial investigation (Laurin et al., 2009)
failed to detect a significant association with our sample of 241 TS-affected probands,
our TS sample has since expanded to include 377 TS-affected children. For this reason
we conducted a follow-up association study, re-evaluating the original three markers as
well as more recent markers identified with trends for association with ADHD near
SLC1A3, for association with TS using our larger sample.
The results of our investigation revealed a trend for marker rs2562571 with TS
(p = 0.025): a marker that was selected based on an ADHD GWAS association signal
(Neale et al., 2010). This SNP is located in the intergenic region, approximately 8.6
kilobases upstream of SLC1A3 and has no known function. Using the publically available
HapMap database, we were unable to identify any markers in high LD with marker
rs2562571. Therefore, it is difficult to determine the significance of this finding. Since
our finding did not withstand correction for multiple tests it may not reflect a true
association. Haplotype analysis failed to identify significant global haplotypes, while two
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individual haplotypes consisting of markers rs2562571/rs4869675 and
rs2269279/rs1529461 showed trends of association with the TS sample.
A published study of SLC1A3 using 299 ADHD subjects reported a significant
association of a two-marker haplotype (rs2269272/rs2032893) spanning approximately
11 kilobases (Turic et al., 2005). Although we were unable to find a significant
association of this particular haplotype with TS using our sample (not shown), one of the
haplotypes that demonstrated a trend in our study, rs2269272/rs1529461, lies within an
ADHD associated region reported by Turic and colleagues. Considering that this region
has been implicated in ADHD, and was highlighted using our TS sample, it is plausible
that it harbors risk alleles relevant to both TS and ADHD. Using a subset of our TS
sample that was included in the GWAS of TS we were unable to find any positive
findings that would point to further studies. The lack of positive association signals from
this region may have been the result of an underpowered study; therefore this region
remains of interest.
5.1.3 Global Discussion of SLC1A1 and SLC1A3
For both investigations our strongest signals came from SNPs with no known function.
This is not uncommon in genetic association studies of complex traits, were associated
SNPs are commonly found outside of protein-coding regions (Hindorff et al., 2009).
Considering that the purpose of genetic association studies is to identify genetic variants
that can clarify the mechanisms underlying pathophysiology of a particular trait or
disorder, it is important to identify associated variants that can result in functional
changes.
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Mutations of the coding regions of SLC1A1 and SLC1A3 do not account for
reported association signals, or the trends observed in the present study. Associated
variants that are found within regulatory elements may influence susceptibility by altering
transcription factor binding. Under the assumption that association signals from other
studies and the trends of this study reflect true associations, we hypothesized that variants
of SLC1A1 and SLC1A3 were influencing gene regulation by altering transcription factor
binding sites. Integration of the ENCODE regulatory track and the UCSC browser
allowed for the identification of regulatory elements. Specifically, we used enrichment of
histone marks H3K4Me1, H3K4Me3 and H3K27Ac to indicate promoter or enhancer
regions.
In the present study, four of the five markers that produced trends (either through
single-marker or haplotype) did not map to putative regulatory regions. For this reason,
potentially functional variants (i.e., alleles that tag putative regulatory regions) that are in
LD with associated markers were also tested for association; however, none of the
additionally genotyped SNPs produced any evidence of association with TS using our
sample.
While the co-occurrence of TS, ADHD, and OCD in the same individual is likely
influenced by genetic factors, identifying a single susceptibility locus that has been
implicated in all three disorders has been challenging. For instance, despite conducting a
PubMed search using the words “Tourette syndrome” “ADHD” “OCD” “GWAS” and
“linkage” it was difficult to identify candidate genes based on strong positional evidence
for all three disorders. This is not entirely unexpected given that most linkage studies of
these disorders have only produced weak evidence of linkage, and sub-threshold findings
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are less likely to be published. Despite the number of linked regions that have been
reported for these disorders, evidence of overlapping risk loci is lacking. Recent efforts to
identify genetic variants conferring susceptibility through GWAS studies have also failed
to identify shared susceptibility loci for TS, ADHD and OCD. This may in part be due to
the relatively small sample sizes of the first GWAS of TS and OCD, which included 1285
cases and 1465 cases with 400 trios, respectively. While these GWAS collaborations
have resulted in the largest TS- and OCD- samples to date, it is now clear that more
substantial sample sizes will be required to detect susceptibility loci (Schizophrenia
Working Group of the Psychiatric Genomics Consortium, 2014). For example, a recently
published schizophrenia GWAS of 36,989 cases and 113,075 controls identified 83
additional susceptibility loci, a large increase from the 25 risk loci that had been
previously found using much smaller sample sizes. Collaborative efforts of consortiums
of TS, ADHD and OCD are currently underway to acquire much larger sample sizes to
improve the power of GWAS studies. Therefore, while common susceptibility loci fro all
three disorders remains elusive, future GWAS collaborations will likely highlight
possible genetic candidates based on position.
5.2 Global Limitations
The purpose of the SLC1A1 and SLC1A3 studies were to identify common genetic risk
variants across TS and ADHD. To improve the probability of detecting a functionally
relevant polymorphism, we systematically selected SNPs that located to predicted
promoter or enhancer regions derived from ENCODE histone marks H3K4Me1,
H3K4Me3 and H4K27Ac; however, the use of ENCODE histone marks to predict
regulatory elements are not without limitations. The human body is composed of a wide
83
variety of cell-types shaped by differential gene expression (Vaquerizas, Kummerfeld,
Teichmann & Luscombe, 2009; Lee et al., 2012). Neural cell-types are no exception to
this and have cell-type specific expression of transcription factors (Ernst et al., 2011).
One of the limitation of using histone mark signals from ENCODE, is that they are
derived from non-neural cell lines. Therefore, histone mark signals should be interpreted
with caution when drawing assumptions of regulatory elements in the brain.
Complex genetic traits often have multiple causal variants with small effects
contributing to susceptibility (reviewed in Glazier, Nadeau, & Aitman, 2002; Manolio et
al., 2008; Hindorff et al., 2009). Association studies of diseases of polygenic inheritance
generally identify risk variants with effect sizes greater or less than 1.2 (Schanze et al.,
2011; Wiste et al., 2014). For the SLC1A1 study we would only have been able to detect
an association for a genetic variant with a relative risk of 1.75-1.96 for TS and 1.65-2.01
for ADHD using our respective samples. For the SLC1A3 study, we only had adequate
power to detect relatively large effect sizes for TS. Both TS and ADHD are proposed to
be due to polygenic inheritance with multiple common risk variants of small but additive
effects. It is plausible that the actual genetic effect sizes were too low to detect given our
relatively modest samples sizes. Under these assumptions non-significant trends observed
in our studies may be due to insufficient power to detect variants of small effect, and may
reflect true associations. However, to confirm these assumptions, meta-analysis or larger
samples in respective samples would be required.
A methodological limitation of this study derives from the use of the family-based
association test, TDT. While this test controls for population stratification by using
parental genotypes of affected offspring, only transmissions of heterozygous parents are
84
informative. Therefore, although our entire respective samples consisted of 377 TS-
affected probands and 247 affected-ADHD probands, only a proportion of the parental-
offspring transmissions were informative. This may in part account for our lack of
positive findings, but can be addressed by using larger sample sizes.
The composition of our TS sample was also a challenge of this study. Many of
our TS probands had also been diagnosed with comorbid ADHD or OCD, with only a
small proportion (32%) of probands having a diagnosis of TS only. This was not entirely
unexpected as TS is rarely found without comorbid conditions. However, our TS sample
was more reflective of a TS+ADHD sample. Once again this was not entirely unexpected
considering that the prevalence of comorbid ADHD with TS is substantially higher than
that of TS without ADHD in both clinical and population based studies. The presence of
subgroups could have been beneficial given a larger sample size. Stratification into more
homogeneous groups (TS only, TS + ADHD, TS + ADHD + OCD, TS + OCD) would
have allowed us to compare the association signals of the genotyped variants across
subgroups; however, considering that none of our findings remained significant after
corrections for multiple tests using our entire sample, analysis of these subgroups would
require a much larger sample to ensure adequate power.
5.3 Future Direction
5.3.1 SLC1A1
While we were unable to detect a significant association between SNPs within predicted
enhancer regions and our TS and ADHD samples, it is plausible that DNA changes to
other cis-regulatory elements of SLC1A1 may account for reported associations or trends.
85
For instance, while alternatively spliced transcripts do not always result in dysfunctional
proteins (reviewed in Tazi, Bakkour & Stamm, 2009), a recent study identified an
isoform transcript of eaat3 that lacks exon 11 in the brain (Porton et al., 2013). The
resulting protein from this transcript has a truncated carboxylic end—an alteration that
has been proposed to interfere with the assembly of the glutamate transporter trimer
(Porton et al., 2013) and reduce the transporter’s affinity for glutamate (Maragakis &
Rothstein, 2004).
The haplotype detected in our study, rs301434/rs301435/rs3087879, spans 10kb
overlapping the 11th exon and 3’ untranslated region. Based on association signals from
this region in studies of OCD, and the trends observed using our TS and ADHD samples,
it is plausible that DNA sequence changes involving these variants play a role in pre-
mRNA splicing. Another possibility is that the causal variants lie within this region and
influence the regulation of SLC1A1 through post-transcriptional effects.
Alternatively splice transcripts are the result of a process called splicing. Splicing
involves the removal of introns from pre-mRNA transcripts and is regulated by binding
of spliceosomes to specific regions of the DNA (splice regulatory elements). Mutations to
regulatory elements including intronic splice enhancer have been associated with disease
and exon skipping (Wei, Lin, Modafferi et al., 1997; McCarthy and Phillips, 1998).
Therefore, changes to DNA sequences where splice regulatory proteins bind may account
for the alternatively spliced transcript reported by Porton et al.
Publically available databases including the Human Splice Finder
(http://www.umd.be/HSF/) may be used to evaluate this region for splice regulatory
86
elements. Future studies using larger samples are also required to determine whether
signals from this study reflect true findings.
5.3.2 SLC1A3
Future investigations should evaluate marker rs2562571 using an independent
sample of TS. Assuming this finding is replicated, future studies should attempt to clarify
the underlying mechanism of this SNP. One possibility is that it is located within an
enhancer region specific to neural tissue. Enrichment of histone marks sequenced from
neural tissue can be used to determine whether enhancer activity is present at or near this
locus. Further, luciferase gene assays may be used to determine whether marker
rs2562571 coincides with SLC1A3 expression levels.
Haplotype analysis using our TS sample highlighted an ADHD associated region.
Assuming that our trend represents a true finding, it is plausible that markers within this
haplotype region can account for the observed association signal. Using the genome
browser and ENCODE transcription factor binding track we were unable to find a
putative enhancer element within the haplotype region; however, we did identify a
putative insulator element. Insulator elements are also involved in the regulation of gene
transcription. Unlike enhancer elements, which are involved in facilitating transcription
upon binding of transcription factors, insulator elements may block enhancer activity of
distal genes (reviewed in Burgess-Beusse, et al., 2002). We were able to identify a single
marker, rs1122900, located within this putative insulator element that has a MAF greater
than 30%. Using HapMap data we were able to determine that this marker is more
strongly correlated with single-markers rs2269272 and rs1529461 than those markers are
with each other. Considering the correlation of marker rs1122900 with markers
87
rs1529461 and rs2269272, as well as its functional relevance, it is a robust candidate to
explain the haplotype signal detected in this investigation. Further study is required to test
this marker for association using TS and ADHD samples. Under the assumption that a
significant association signal is detected, subsequent investigations should also test this
polymorphism for regulatory activity in neural tissue.
As noted earlier it would take 27 tagSNPs (r2 > 0.80) to adequately capture the
SLC1A3 gene. In this study only 10 markers were tested for association. It is plausible
that other polymorphisms of this gene contribute to TS, and that the GWAS results from
the subset of TS samples lacked adequate power to detect association signals. Therefore,
markers that tag the result of this gene should also be tested for association with TS
samples.
The present investigation examined SLC1A3 in TS and ADHD. However, TS and
ADHD are likely mediated by OCD. To our knowledge, the 5p13 region has not been
implicated in OCD. Nevertheless, SLC1A3 may contribute to OCD susceptibility, as
multiple candidate genes are assessed based on function without positional evidence.
Furthermore, variants associated with disease do not always produce an association signal
in every study or reach the threshold for genome-wide significance (Xu & Taylor, 2009;
Yeager et al., 2007; Thomas et al., 2008). It is plausible that studies of OCD to date have
not had adequate power to detect an association signal at the 5p13 region because its
effect size is very small. Based on the trends observed in the TS sample of this study and
other investigations of ADHD, as well as its involvement in the glutamatergic system,
association testing of SLC1A3 is required using OCD samples.
88
5.3.3 Future Candidate Genes for Study
As mentioned earlier, it is plausible that the trends observed in this study reflect positive
findings that are limited by the lack of power (discussed further above in Global
Limitations). Alternatively, the lack of significant findings from this study may suggest
that SLC1A1 and SLC1A3 are not involved in TS or ADHD.
A fairly novel gene of study is protein tyrosine phosphatase receptor type D,
PTPRD. PTPRD is encoded on chromosome 9p24, in the same region as SLC1A1. In
addition to being involved in the differentiation of glutamatergic synapses, and
interacting with other highly studied genes (e.g., SLITRK3), PTPRD has been implicated
in OCD (Matthesien et al., 2014) and ADHD (Elia, Gai, Xie et al., 2010), but as not been
examined in TS. Therefore, PTPRD is an attractive gene for study in the search for
common susceptibility loci for TS, ADHD and OCD.
5.4 Conclusion
SLC1A1 and SLC1A3 play important roles in glutamate regulation in the brain. While
we were unable to produce concrete evidence that the genes of these glutamate
transporters are associated with TS and ADHD, we cannot rule out that they contribute to
these disorders. Alterations to gene expression provide a plausible explanation of the
mechanisms underlying the observed trends and reported association signals. Specifically,
identification of regulatory elements using neural tissue and studies using larger samples
will allow for further insight as to whether SLC1A1 and SLC1A3 are risk genes for TS
and ADHD.
89
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