GENOME-WIDE ASSOCIATION STUDY OF PERSISTENT DEVELOPMENTAL STUTTERING

BY

SHELLY JO KRAFT

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Speech and Hearing Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2010

Urbana, Illinois

Doctoral Committee:

Associate Professor Nicoline Ambrose, Chair, Co-Director of Research Professor Nancy J. Cox, Co-Director of Research, University of Chicago Professor Emeritus Ehud Yairi Assistant Professor Laura DeThorne Associate Professor Stephanie Ceman

Abstract

A genome-wide association study (GWAS) is an approach that involves scanning markers across complete sets of DNA, or genomes, of many people to indentify genetic variations associated with a particular disease. A GWAS uses unrelated individuals, and for the purposes of this study, only those who persistently stutter, to identify common genetic variants among these people when contrasted to the general population of matched ethnicity. Eighty-four affected subjects, ages 13 to 70 years, of northern European ancestry and 107 matched controls were investigated to identify replicable candidate that influence the risk of individuals to stutter. The results associated 10 significant candidate genes with persistent developmental stuttering. In addition, the pathways in which these genes function to potentially disrupt the fluent production of speech were investigated. Functional significance was found with relevance to three functional categories pertaining to neural development, neural function, and behavior. Many of the genes were also found to be implicated in other known neurological, behavioral, and speech and language disorders.

ii Table of Contents

List of Tables ...... vi

List of Figures...... viii

Chapter 1 Introduction ...... 1

Chapter 2 Literature Review...... 5

Familial Incidence ...... 5

Family Aggregation Studies ...... 6

Twin Studies ...... 11

Genetic Models of Stuttering...... 15

Segregation Analysis ...... 15

Sex-specific Heritability ...... 15

Biological Genetics...... 21

Identification of Specific Genes...... 21

Linkage Analysis...... 22

Genome-Wide Association...... 26

FOXP2 ...... 28

Behavioral Genetics ...... 30

The Environment ...... 30

Theories, Rationale, and Hypothesis ...... 32

Theories...... 32

1. Final Common Pathway Theory...... 33

2. Oligogenic Theory...... 35

3. An Addiction Model...... 36

Goals ...... 39

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Rationale ...... 39

Hypothesis...... 40

Chapter 3 Methods ...... 42

Subjects ...... 42

Selection Criteria: Diagnosis of Stuttering ...... 43

Data Collection / Data Access...... 44

Extraction ...... 45

Genotyping ...... 46

Quality Control...... 47

Principal Components Analysis...... 48

Data Analysis...... 49

Statistical Analysis ...... 49

Primary Analysis...... 49

Candidate- Analysis...... 50

Chapter 4 Results...... 51

Principal Components Analysis ...... 51

Association Analysis ...... 52

Statistical Analysis...... 54

Candidate Gene Analysis ...... 58

Chapter 5 Discussion ...... 64

Neural Developmental Genes ...... 66

Neural Function Genes ...... 68

Behavioral Genes...... 70

Cross-Involvement ...... 73

Autism ...... 74

iv

Alzheimer’s disease...... 76

Study Limitations ...... 80

Conclusions...... 82

Future Directions ...... 82

Acknowledgements ...... 83

References ...... 84

v

List of Tables

Chapter 1 Introduction

Chapter 2 Literature Review

Table 2.1 Percentages of Affected 1st degree Relatives reported by

Andrews and Harris (1964) ...... 6

Table 2.2 Percentages of Affected 1st degree Relatives reported by

Kidd (1984) ...... 8

Table 2.3 Percentages of Affected 1st degree Relatives reported by

Ambrose, Yairi and Cox (1993) ...... 8

Table 2.4 Percentages of Affected 1st degree Relatives reported by

Janssen, Kloth, Kraaimaat, and Brutten (1996) ...... 10

Table 2.5 Percentages of Affected 1st degree Relatives reported by

Yairi and Ambrose (2002) ...... 11

Table 2.6 A Summary of Twin Studies Reporting Concordance Rates

of Stuttering ...... 14

Table 2.7 Summary of Linkage Studies ...... 25

Table 2.8 Genes Associated with Alcohol and Drug Addiction ...... 37

Chapter 3 Methods

Table 3.1 Demographic Data of Participants...... 43

Chapter 4 Results

Table 4.1 Statistically Significant SNPs Showing Association

Persistent Developmental Stuttering...... 55

Table 4.2 Candidate Genes/SNPs in Association with Persistent

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Developmental Stuttering ...... 59

Table 4.3 GWAS overlap with previous studies...... 61

Table 4.4 Overlap genes with other disorders...... 62

Chapter 5 Discussion

Table 5.1 Functional categories of candidate genes ...... 65

Table 5.2 Genetic overlap of candidate genes with other disorders ...... 73

vii

List of Figures

Chapter 1 Introduction

Chapter 2 Literature Review

Chapter 3 Methods

Chapter 4 Results

Figure 4.1 EIGENSTRAT Analysis of Ancestry ...... 52

Figure 4.2 Original Quantile-Quantile Plot of European Ancestry ...... 53

Figure 4.3 Corrected Quantile-Quantile Plot of Strict Northern

European Ancestry ...... 54

Figure 4.4 Manhattan Plot of SNPs ...... 57

Figure 4.5 Manhattan Plot of Candidate Genes/SNPs in

Association with Persistent Developmental Stuttering ...... 60

Chapter 5 Discussion

viii

Chapter 1 Introduction

The disorder of stuttering currently affects an estimated 50 million people worldwide (approximately .73% of the current population according to Craig, Hancock, Tran, Craig, & Peters, 2002). More individuals in the United States are affected with developmental stuttering than with AIDS, schizophrenia, Alzheimer’s disease, or

Parkinson’s disease (Cates, 1999; Evans, 1990; Goldner, Hsu, Waraich,

& Somers, 2002; Lilienfeld, Chan, Ehland, Godbold, Landrigan, Marsh, et al., 1990). Yet when compared to the research conducted on the etiology of these diseases, it becomes apparent that scientists know relatively very few details about the cause of stuttering.

Theories developed as early as the 19th century have incorporated the presumption that stuttering is heritable (see review by Van Riper,

1982). For over 80 years, scientists have noted the disorder’s predisposition to cluster in families. The earliest genetically oriented studies focused on family incidence (Gutzman, 1924; Bryngelson &

Rutherford, 1937; West, Nelson, & Berry, 1939; Nelson, 1939).

Throughout the following eight decades, researchers used family pedigrees, in the form of aggregation studies (Andrews & Harris, 1964;

Ambrose, Yairi, & Cox, 1993; Janssen, Kloth, Kraaimaat, & Brutten,

1996; Kidd, 1984; Yairi & Ambrose, 2002) and twin studies (Carroll,

1965; Dworzynski, Remington, Rijsdijk, Howell & Plomin, 2007; Graf, 1

1940; Nelson, Hunter & Walter, 1945; Godai, Tatarelli, & Bonanni, 1976;

Howie, 1976, 1981; Koch, 1966), to statistically investigate the hereditability of the disorder. Aggregation and twin studies could be conducted without the use of DNA to determine heritability. These studies were the first to document a high incidence of stuttering among first degree family members, and significantly higher concordance rates among monozygotic twins than among dizygotic twins. This was sufficient to conclude that stuttering was “highly heritable” with little environmental effects (Dworzynski, et al., 2007).

Segregation studies were then employed to match a transmission model to the pedigrees. Researchers needed to know how stuttering was passed from one generation to the next. Segregation studies, like aggregation and twin studies, do not require the use of actual DNA, and could be carried out by utilizing detailed pedigrees. The results of these studies remained inconclusive with several potential transmission models proposed (Andrews & Harris, 1964; Ambrose, Cox, & Yairi, 1997;

Cox, 1993; Kidd, 1980, 1984; Kidd & Records, 1979; Kidd & Kidd, 1978;

Kidd, Heimbuch, & Records, 1981; MacFarlane, Hanson, Walton &

Mellon, 1992). The most consistent finding throughout the segregation studies was regarding sex influence, however, even this had some discrepancies (Ambrose, Cox, & Yairi, 1997).

Technology advanced in the early 2000s allowing for scientists to use blood samples in an attempt to identify specific genes implicated in

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the disorder of stuttering. Several linkage studies were carried out using concentrated populations of affected families found in various locations across the globe (Riaz, Steinberg, Ahmad, Pluzhnikov, Riazuddin, Cox, et al., 2005; Shugart, Mundorff, Kilshaw, Doheny, Doan, Wanyee, et al.,

2004; Suresh, Ambrose, Roe, Pluzhnikov, Wittke-Thompson, Ng, Cook, et al., 2006). These studies, however, also produced inconsistent findings for the genes implicated, potential transmission models, and sex influence.

The summation of aggregation, twin, segregation, and linkage studies have contributed to the basic understanding that the underlying cause of stuttering is genetically based. The evidence does not dispute the strong heritability of the disorder. It does, however, suggest that stuttering is complex, that the transmission model is complex, and that the role of sex in the disorder is also complex.

Unfortunately, some important questions still remain, such as what genes are involved and how do these genes produce the phenotypic characteristics of developmental stuttering? Instead of using the same strategies of previous researchers and potentially coming up with additional models of transmission and theories of sex influence, this study aims to utilize a different type of genetic technology newly available for the research of complex disorders. A genome-wide association study

(GWAS) is an approach that involves scanning markers across complete sets of DNA, or genomes, of many people to indentify genetic variations

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associated with a particular disease. This type of study does not follow genes through generations of family members currently or previously affected with the disorder. In contrast, a GWAS uses unrelated individuals, and for the purposes of this study, only those who persistently stutter, to identify common genetic variants among these people when contrasted to the general population of matched ethnicity.

Moreover, the aim of this study is not only to identify replicable candidate genes that influence the risk of individuals to stutter, but also to investigate the pathways in which the genes function to disrupt the fluent production of speech.

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Chapter 2 Literature Review

Familial Incidence

Studies investing familial incidence are designed to answer the most basic genetic question underlying a disorder: Does this disorder run in families? Bryngelson and Rutherford (1937) compared families containing affected probands who stutter with families containing control

(unaffected) probands and found nearly a 3-fold increase in incidence among the experimental group (46% to 18%). Bryngelson and colleagues followed with several similar studies which confirmed that risk for stuttering increases with relatedness to an affected individual. Andrews and Harris (1964) and Kidd (1977) employed family studies and supported earlier findings that the risk of stuttering is greater within the families containing a person who stutters than in the general population.

In a review of 28 familial incidence studies, Yairi, Ambrose, and Cox

(1996), reported that a majority of those studies found familial stuttering in 30-60% of probands, compared to less than 10% in the families of control probands. In their own study, Ambrose, Yairi, and Cox (1993) found 28% of children who stutter to have a parent who also stutters,

43% have an affected member in their immediate family (siblings and/or parents), and 71% to have stuttering in their immediate family or in their

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extended family.

Family Aggregation Studies

Aggregation studies focus on distribution patterns of a disorder throughout familial pedigrees. They meticulously take into account relatedness of family members and family size. Specifically, they not only calculate the number of affected members within a family (as in familial incidence studies), they also focus on the relationship any given affected/non-affected member has to an affected proband. Andrews and

Harris (1964) were the first to perform an aggregation study on stuttering. They looked at male and female affected probands and calculated the incidence of stuttering found among their fathers, mothers, brothers, and sisters. Two hundred and fifty six pedigrees from probands age 2 years to adult were surveyed. Percentages of first-degree affected relatives of male and female probands are shown in Table 2.1.

Table 2.1 Percentages of Affected 1st degree Relatives reported by

Andrews and Harris (1964)

Sex of Proband Fathers Mothers Brothers Sisters

Male 17.6 6.4 18.7 6.1

Female 25.7 8.3 29.9 19.4

6

Interestingly, the study reported a significantly higher incidence of

stuttering among the male relatives of either sex proband (with the

exception of affected sisters of female probands, which was comparable

to male relatives). However, the greatest risk of stuttering occurred for

relatives of female probands. The average non-sex specific risk of any

first-degree relative of a female proband to be affected was reported at

20.2%1. The risk of being an affected male relative of an affected female

was even higher as observed in very high percentages of affected fathers

and brothers of females who stutter. In contrast, male probands had a

non-sex specific risk of 12.2% for having an affected first-degree relative.

This is nearly half the reported risk for female probands. Female relatives

of males who stutter were at the lowest risk for stuttering.

Very similar results were reported by Kidd (1984) in a replication

study of Andrews and Harris’ (1964) study. Information was gathered

from 600 complete pedigrees of affected adult probands. Familial

incidence and aggregation information was compiled for these families

(see Table 2.2).

1 This number is calculated by averaging all first-degree relative stuttering risk percentages of female probands. Male proband non-sex specific stuttering risk was also calculated accordingly. 7

Table 2.2. Percentages of Affected 1st degree Relatives reported by Kidd

(1984)

Sex of Proband Fathers Mothers Brothers Sisters

Male 18.4 4.4 19.4 4.1

Female 20.4 11.7 23.1 12.8

The percentages of incidence for affected first-degree relatives were nearly identical to Andrews and Harris (1964). The study confirmed the earlier presented findings that relatives of females were at a much higher risk of stuttering than the relatives of male probands.

Ambrose, Yairi, and Cox (1993), investigated the frequency of first degree affected relatives of 69 affected pre-school age probands (ages 2-6 years). Aggregation for this study is reported in Table 2.3.

Table 2.3 Percentages of Affected 1st degree Relatives reported by

Ambrose, Yairi and Cox (1993)

Sex of Proband Fathers Mothers Brothers Sisters

Male 31.3 4.2 24.3 10.7

Female 4.8 4.8 33.3 40.0

8

Numbers observed in this study indicate a greater risk for affected relatives of male probands than previously reported by Andrews and

Harris (1964), and Kidd (1984). Non-sex specific risk rates were 17.63% for relatives of male probands, and 20.7% for relatives of female probands. The difference in sex ratio findings were not statistically significant. Unlike the two previous studies, Ambrose et al. (1993) used child-age probands instead of adults. They stated that their sample

“provided a considerably more accurate representation of the affected population” as it included “children who recovered naturally, who would have been unidentified or excluded from other studies” (Yairi & Ambrose,

2002, p. 12).

Janssen, Kloth, Kraaimaat, and Brutten (1996) performed a replication study of Ambrose et al. (1993), with adult probands that specifically aimed at investigating the relationship between the gender of stuttering probands and the incidence of stuttering among their relatives.

Detailed pedigrees from 106 affected adults were analyzed and the results are reported in Table 2.4 More affected brothers and fathers were reported for both male and female probands, a trend that is also evident in previous studies. While percentages of sisters of affected probands mirror those reported in Andrews and Harris (1964), the non-sex specific risk rates for relatives of female probands was nearly equal to the rate for relatives of male probands (16% and 15.1% respectively). The researchers concluded that they were in agreement with Ambrose et al.

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(1993). Their evidence did not support the increased likelihood for relatives of female probands to be at a higher risk for stuttering than those of male probands.

Table 2.4 Percentages of Affected 1st degree Relatives reported by

Janssen, Kloth, Kraaimaat, and Brutten (1996)

Sex of Proband Fathers Mothers Brothers Sisters

Male 22.1 5.2 23.3 9.7

Female 24.1 0 20.3 19.6

Yairi and Ambrose (2002) found the brothers of female probands to be at a greater risk for stuttering than reported by the four previous aggregation studies (see Table 2.5). This finding, however, did not significantly influence the overall non-sex specific risk ratios for first- degree relatives of either proband to deviate from previously reported trends. Risk for relatives of males was calculated to be 18.6%, and 21.4% for females; demonstrating less than a 3% difference between the two affected genders.

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Table 2.5 Percentages of Affected 1st degree Relatives reported by Yairi

and Ambrose (2002)

Sex of Proband Fathers Mothers Brothers Sisters

Male 25.7 5.7 28.6 14.7

Female 15.2 6.1 50.0 14.3

In summary, the aggregation studies demonstrate a markedly high incidence of stuttering among first degree family members, with a trend toward affected male relatives of either male or female proband to be at a slightly higher risk for stuttering than female relatives (average father risk 20.5%, brother risk 27.1%, mother risk 5.7%, and sister risk

15.14%). In contrast, if one isolates the most recent studies from the entire body of literature on this topic, it is evident that they report the non-sex specific risk for stuttering to be approximately equal for relatives of male and female probands.

Twin Studies

While familial incidence and family aggregation studies appear to be quite compelling in supporting the argument for stuttering heritability, some argue that the evidence could suggest otherwise. For example, religion, dialects, attitudes and beliefs also run in families

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without having a genetic etiology. In search of answers, scientists turned to twin studies.

Twin studies have been employed for the last 65 years to investigate the heritability of stuttering and potential environmental influences. Concordance of stuttering in twin pairs has been calculated by several researchers (Carroll, 1965; Dworzynski, Remington, Rijsdijk,

Howell & Plomin, 2007; Graf, 1940; Nelson, Hunter & Walter, 1945;

Godai, Tatarelli, & Bonanni, 1976; Howie, 1976, 1981; Koch, 1966) in an attempt to partition genetic variance of stuttering from environmental effects.

Andrews, Morris-Yates, Howie, and Martin (1991) questioned 2438 twin pairs about their stuttering history. The twins were listed in an

Australian twin registry and contacted first by mail then by telephone interview. They reported a 71% estimate of hereditability for additive genetic variance (genetic alleles at two or more gene loci producing combined effects equal to the sum of their individual effects). Felsenfeld,

Kirk, Zhu, Statham, Neale, and Martin (2000), found nearly identical results to those of Andrews and colleagues (1991), they also used telephone interviews to diagnose 457 individuals who stutter from an

Australian twin registry. Using a screening tool, they found approximately 70% of genetic variance in stuttering to be due to additive genetic effects, and the remaining 30% due to non-shared environmental effects.

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Sheehan and Costley (1977) in a review of twin literature from

1937-1976, reported concordance rates far below those reported in more recent studies (see Table 2.6 below). Godai, Tatarelli, and Bonanni (1976) investigated 63 twin pairs (22 Monozygotic and 41 Dizygotic), with one or both members affected. The study’s findings showed a high concordance of stuttering in monozygotes 83% and only 10.5% in dizygotes. Howie

(1981) used interviews to investigate same sex twins, and found concordance to be similar to Godai et al. (1976), with concordance rates of 63% in monozygotes and 19% in dizygotes. These findings suggest very strong evidence for genetic heritability in stuttering. Despite the lack of perfect concordance of stuttering among monozygotic twins, the stronger correlation is notably higher than that for fraternal (dizygomatic) twins.

In light of this, Cox (1988) emphasized that the absence of perfect concordance in identical twins indicates that other factors beyond simple genetic transmission must play a role in the etiology of stuttering. These other factors may be present environmentally. A shared environment, however, may not be the source. Dworzynski, et al. (2007) found very little to support the involvement of shared environment effects in heritability. Their study assessed concordance rates, heritability, and age related predictability of persistence or recovery of stuttering. Their findings supported significantly higher concordance rates among monozygotes than among dizygotic twins. In agreement with previous studies, their findings support the assumption that stuttering is “highly

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heritable” with very little shared environment effects.

Table 2.6 A Summary of Twin Studies Reporting Concordance Rates of

Stuttering

Investigator Year Subjects Findings MZ DZ

Berry 1937, 250 twin pairs Stuttering occurs na na 1938 more frequently in twins than in non- twin population Nelson, Hunter 1945 200 twin pairs Concordance Rates 14% 23% & Walter

Graf 1955 552 twin pairs Concordance Rates 11% 22%

Carroll 1965 1958 cws, both Stuttering occurs at na na twin and non- equal incidence twin rates between groups Koch 1966 90 twin pairs Concordance equal between dizygotes and monozygotes Howie 1976 42 twin pairs Concordance Rates 58- 13- 63% 19% Godai, Tatarelli, 1976 63 twin pairs, Concordance Rates 83% 10.5 & Bonanni one or both % affected Howie 1981 30 twin pairs Concordance Rates 63% 19% Andrews, 1991 135 twin pairs Concordance Rates 20% 5.4 Morris-Yates, with one or % Howie, & Martin both affected Felsenfeld, Kirk, 2000 457 affected Concordance Rates Zhu, Statham, twins or co- Screening Method 17% 6% Neale, & Martin twins Interview Method 45% 15% Dworzynski 2007 1,025 twin Concordance Rates Remington, children Age 3 26% 9% Rijsdijk, Howell affected Age 4 32% 12% & Plomin 11,867 twin Age 7 29% 3% control unaffected Portions of this table are adapted from Sheehan & Costley, 1977, p.55.

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Genetic Models of Stuttering

Segregation Analysis

Segregation analysis is applied to pedigrees of interest in an attempt to match genetic models to the observed patterns of transmission of a trait within families. Complex segregation analysis uses a statistical model to dissect major gene effects in diseases where abnormal penetrance, mutation carriers, and non-Mendelian disease transmission is probable (Bonney, Lathrop, & Lalouel, 1988).

Segregation patterns studied within pedigrees of stuttering probands have reinforced the need for complex analysis to investigate the transmission of this disorder. Results from aggregation studies, paired with a male-favored sex ratio of incidence and prevalence, has long fueled a debate among scientists regarding the influence of gender in segregation models.

Sex-specific Heritability

Initially, childhood incidence of stuttering is observed to have a sex-effect of 2:1 or even smaller male-to-female ratio (Mansson, 2000;

Yairi, 1983). As the disorder progresses into adulthood, more females reportedly recover from stuttering and the ratio becomes closer to 4:1 or

5:1 (Bloodstein, 1995; Yairi & Ambrose, 1992, 1999, 2005; Yairi,

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Ambrose, & Cox, 1996). Ambrose et al. (1993) specifically reported that more girls recover from stuttering than boys. These observations have led researchers to believe to that there may be sex-specific genes responsible for stuttering and potentially for natural recovery.

The first segregation analysis was attempted by Andrews and

Harris (1964) in an effort to identify genetic transmission of stuttering among their 256 detailed pedigrees. Their definitive findings suggested a model with sex-modified expression, but the specifics appeared complex and unclear. It took nearly 15 years for the next segregation analysis in stuttering to be attempted. In agreement with Andrews and Harris

(1964), Kenneth Kidd first proposed a sex-modified trait hypothesis for stuttering transmission in 1978. The initial study investigated 51 affected probands and their first degree relatives. The experiment was performed at Yale University and was followed by larger subsequent studies known as the Yale Studies, all supporting the sex-modified theory (Kidd, 1980, 1984; Kidd, Heimbuch, & Records, 1981). Kidd and

Records (1979) defined stuttering as a complex trait that could be transferred by a number of sex-modified multifactorial models.

Kidd (1981) used 386 probands and their first degree relatives to demonstrate vertical transmission of stuttering in families and support the previous hypothesis of susceptibility to sex-modified expression. The pedigrees used in this study specifically suggested a sex-modified polygenic threshold model (this was a minor adjustment to the previously

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suggested single-gene or polygenic transmission model (Kidd (1980)).

Diseases or traits for which the genetic component is attributable to many genes acting additively are referred to as polygenic (e.g fingerprint ridges (Mendenhall, Mertens, & Hendrix, 1989)). These genes may be on the same or on several different . In an additive model, the contribution of the alleles are additive. For example, consider an additive gene affecting height in which the A allele has no effect on height, but the a allele increases height by 1 cm. Individuals with the AA genotype have no effect of genotype at the A gene on height while heterozygous individuals (Aa) have, on average, a 1 cm increased height. An individual homozygous for a (aa) would have according the additive effect, on average, a 2 cm increased height. In a polygenic model many genes contribute to a phenotype in this additive way. The effects are simply additive across genes as well. If a second gene, B also affects height, with the B allele having no affect on average height and the b allele increasing height by 2 cm, someone who has the AaBb genotype would be 3 cm taller than the average.

Some phenotypes are multifactorial, which means they are not only polygenic but also influenced by many non-genetic factors, such as the pre- and post-natal environment (e.g height (Karlin, Williams,

Jensen, & Farquhar, 1981) and skin color). Some traits are influenced by the sex of the individual; this is often documented as dimorphic traits.

Some dimorphic multifactorial traits, such as cleft lip/cleft palate

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(Christensen and Mitchell, 1996; Farrall & Holder, 1992; Mitchell &

Risch, 1992), are more common in males than females (Fogh-Andersen,

1942) and exhibit a threshold effect (Farrall & Holder, 1992). This supports the hypothesis that there must be a certain number of additive genes for the trait to express itself phenotypically. In cleft lip/cleft palate, females exhibit a higher threshold and require more genes for expression. This results in affected females having higher incidences of affected offspring than observed in the offspring of affected males

(Marazita, Goldstein, Smalley, & Spence, 1986). In disorders such as cleft lip/cleft palate and perhaps stuttering, all people are assumed to carry some of the responsible genes. It is the combined effect of both parents’ contributing genes that will either exceed the threshold for expression or result in a very mild disorder to falling within normal limits.

In 1984, Cox, Kramer, and Kidd, proposed a new hypothesis of multifactorial-polygenic transmission with a major loci or locus. A complex segregation model was used to analyze stuttering transmission and the possibility of a single Mendelian locus as a potential explanation was ruled out. This study built upon Kidd and colleagues’ previous ideas and reiterated the role of sex-modified transmission.

MacFarlane, Hanson, Walton and Mellon (1991) investigated five generations of a Utah family demonstrating above average incidence of stuttering rates among its members. The investigators selected a subgroup (two generations) consisting of 38 affected probands and 231

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unaffected relatives. Their findings supported Kidd’s (1981) sex-modified polygenic threshold model.

Ambrose, Cox, and Yairi (1997) did not support the above findings with their segregation analysis of 66 pedigrees and rather suggested a major locus in combination with multifactorial polygenic influences.

Although the study did not include a sex factor in its transmission model, the investigators did report significant differences in recovery rates among males and females. In an earlier study, Ambrose, Yairi, and

Cox (1993) used segregation analysis on 69 affected children (ages 2;1 to

6;3) to test three models of genetic transmission. These were MFP

(Multifactorial /polygenic model), SML (single major locus model), and

MM (mixed model of both MFP and SML). They found statistically significant evidence for a Mendelian major locus component to stuttering transmission. They reported that while their evidence supported the role of a major gene, it remained irrefutable that multiple additional genes were also involved.

Viswanath, Lee, and Chakraborty (2004) also disputed the evidence that a polygenic threshold model alone could result in the observed familial aggregation without a major gene. They applied complex segregation analysis to 47 pedigrees and found transmission of stuttering in the sample to be consistent with an autosomal diallelic major locus model influenced by the two covariates of sex and affection status of the parents.

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Drayna, Kilshaw, and Kelly (1999), purposed that those who stutter belong to one of two groups: a group with a genetic etiology, and a group without genetic etiology (sporadic). Drayna et al. (1999) used the data from 137 males and 87 females of the general public representing all racial groups and demographics. They found that nearly half of the participants had a family history for stuttering. This study supported the findings of Yairi, Ambrose, and Cox (1996) and had a significantly higher number of females who reported a family history of stuttering than males. The sporadic cases were theorized to affect more males than females, in contrast to the genetic cases which are presumed to be equally represented in both males and females.

All of the above studies, except one (Ambrose et al., 1997), suggested theories of sex-modified expression in stuttering transmission.

Studies performed by Ambrose and colleagues, however, did not deny sex as a key feature, as demonstrated by reported sex differences in incidence rates, they merely did not find a sex-modified component in their sample. Gender differences in people who stutter are of interest when considering the disparity in natural recovery rates of males and females. Much of the research done in genetics has targeted some aspect of sex-modification in traits or transmission in an attempt to explain higher male prevalence ratios. This component of many studies, while interesting, has not been irrefutably proven, nor its’ role critical to the discovery of replicatable genes responsible for stuttering.

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In summary, researchers have agreed that simple single-gene fully penetrant transmission models of inheritance of stuttering, such as autosomal dominant, recessive, and x-chromosome-linked transmission models are unlikely (Kidd et al., 1981; Kidd, 1984; MacFarlane et al.,

1991; Cox, 1993; Ambrose et al., 1993; Viswanath, Rosenfield,

Alexander, et al., 2000). Segregation studies investigating stuttering have not conclusively explained transmission of this disorder, but they have provided a starting point for further genetic investigation, namely by means of linkage analysis. The “role of segregation analysis is to provide a specific genetic model for subsequent linkage analysis (Viswanath et al., 2004, p.410).”

Biological Genetics

Identification of Specific Genes

Two main approaches are employed to identify gene-disorder or gene-trait relationships: association and linkage. Both strategies use genetic markers to analyze variations within either families (using linkage) or entire populations (using association). Genetic markers are stretches of DNA that exhibit variations between individuals or populations (McGuffin, Owen, & Gottesman, 2002).

Markers typically fall into two categories: polymorphisms with two alleles and those that are multi-allelic. The word polymorphism simply

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indicates that there is more than one type of DNA sequence at the site, and each DNA sequence type at a site is called an allele. Most two-allele polymorphisms are caused by a mutation exchanging one base within a

DNA sequence for another: e.g. a DNA sequence of AATCG may become

AAGCG, in this example the 3rd base position is polymorphic, with two alleles, a T allele and a G allele. A smaller number of two-allele polymorphisms are actually segments of DNA in which one or more bases have been deleted. Thus, a DNA sequence may exist as AATCG and AAG, in which the middle TC has been deleted. The second type of marker

(multi-allelic) occurs when several different versions of the genetic marker are found within the same population, typically resulting from a portion of the DNA (between 2 and 100 base-pairs long) being repeated in short sequence repeats (SSRs) or longer variable number tandem repeat markers (VNTRs)(Eley & Craig, 2005). Most polymorphisms, whether two-allele or multi-allelic have no consequence to the organism, but may be used in genetic studies as “markers” to help us localize the small number of polymorphisms that actually affect disease to the correct region of the chromosome on which these disease alleles reside.

Linkage Analysis

Linkage analysis traces the inheritance pattern of genes as they are passed from generation to generation. Although the analysis of

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pedigrees in a complex segregation analysis can provide information on the genetic model underlying a disease or trait, geneticists and clinicians hoping to improve diagnosis and treatments for patients are more interested in knowing precisely which genes have polymorphisms that might affect risk of disease. Linkage analysis is one way to determine the genetic location of a trait gene or disease gene. The closer alleles lie along a chromosome the higher their chance of being inherited together. In other words, where they exist regionally and in relation to each other on a single chromosome influences their chance of being linked during cellular reproduction. The goal of linkage analysis is to identify a piece of

DNA in a known chromosomal location that is inherited by all affected family members of the disorder being investigated, and is not inherited by any of the unaffected family members. A co-inherited (or co-segregate) polymorphism is a piece of DNA that can be statistically associated with the disease phenotype within a family. Since linkage analysis uses markers or polymorphisms to define relatively large segments of genetic material likely to contain a polymorphism that causes or contributes to disease, further analysis is needed once a region has been implicated to identify the relevant gene polymorphism.

Several studies have used linkage analysis to investigate families with high numbers of stuttering members. They looked for highly variable genetic markers. Riaz et al., (2005) looked at 44 highly inbred families from Lahore, Pakistan. Using microsatellites, they genotyped

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144 stuttering (affected) and 55 non-stuttering (unaffected) individuals.

Evidence of linkage was found on chromosomes 1, 5, 7, and 12.

Additional analysis was performed on chromosome 12 (using a 5-cM level of resolution). They suggested that an unspecified locus on chromosome

12q (long arm) “may contain a gene with a large effect in this sample” (p.

647). This study included highly inbred individuals, suggesting that the findings may be linked to a common mutational origin for stuttering specific to these families. Hence, they may have different genetic components underlying stuttering than those present in genetically diverse populations. Therefore, the mode of inheritance for disease- associated haplotypes is difficult to clearly identify with confidence.

Shugart et al. (2004) performed a linkage analysis on 68 families from the general population of North America and Europe. Investigating

392 markers across the genome, statistical support was found on chromosome 18. Fine mapping was employed by examining 18 additional markers on Chromosome 18. Several candidate regions, including proximal regions of 18q, were identified with low to moderate levels of statistical significance.

Suresh and colleagues (2006) did a linkage analysis of 100

European-descent probands. They found Chromosome 9 to be moderately linked to a diagnosis of “ever stuttered.” A somewhat weaker finding was on for persistent individuals. The only genome-wide significant findings was for sex-specific linkage on

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chromosome 7 for males and chromosome 21 for females. The study supported sex effects as genetic components to stuttering.

Table 2.7 Summary of Linkage Studies

Most Other Study Year Important Population Chromosomes Chromosome Shugart 2004 18 1, 2, 10, and Compared one et al. 13 model family with 6 affected to 68 families in North America and England Riaz et 2005 12 1, 5, 7, 12 47 highly inbred al. Pakistani families Suresh 2006 9, 15, and Male specific 100 families of et al. conditionally on 7, Female European origin, 2 specific on 21 USA, Sweden, & Israel Wittke- 2007 13 1, 3, 5, 9, 13, One 232 person Thomps 15 family with 48 on et al. ‘affected’ individuals (founder population Hutterites)

Kang, Riazuddin, Mundorff, Krasnewich, Friedman, Mullikin, and

Drayna (2010) re-investigated a region on chromosome 12, using the same initial population used in Riaz et al., 2005 (highly inbred Pakistani families n=47). The study included an additional 77 unrelated affected

Pakistani subjects and 270 affected North American and British subjects.

Using linkage analysis, they found a mutation in the GNPTAB gene in a large Pakistani family (PKST72). Drayna and colleagues were able to

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identify this gene in five other affected individuals from the Pakistani population, and in one North American person of Asian-Indian ancestry.

Three mutations of an additional gene on chromosome 12 (NAGPA gene) were observed in 6 affected North American-British subjects but not in any member of the Pakistani population. GNPTAB and NAGPA govern lysosomal metabolism and are largely associated with cardiovascular, bone, and connective tissues.

Major limitations of linkage studies exist as a result of the relatively low statistical power for samples of complex disorders. The power is drastically influenced by multiple genes and the often hundreds of the chromosomal regions shared among family members not related to the disorder of interest. It can be difficult to narrow the linkage signal in these samples sufficiently to identify a causative gene (Pearson &

Manolio, 2008). For non-Mendelian complex disorders, such as atherosclerosis and asthma and presumably stuttering, a different type of genetic analysis would provide a valuable advantage over family-based linkage studies.

Genome-Wide Association

A genome-wide association study (GWAS) is an approach that involves scanning markers across complete sets of DNA, or genomes, of many unrelated people to find genetic variations associated with a

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particular disease. It is based on the assumption that heritable risk for a common disease is mostly attributable to a relatively small number of genetic variants (Hunter, Altshuler, & Radar, 2008; Pearson & Manolio,

2008). Association mapping studies are designed to identify particular alleles (genetic variants) that are found more frequently in affected individuals than in unaffected individuals. Such alleles either directly affect risk of disease or are in linkage disequilibrium (when alleles are found more or less frequently than expected by random combinations) and physically very close to alleles that directly affect risk of disease.

This systematic study of genetic variations is expected to lead to the localization of causal genes (Hunter et al., 2008) without the need for prior hypothesis regarding genetic associations with the disorder of interest (Hirschhorn & Daly, 2005).

A GWAS allows for very large numbers (906,600) of polymorphisms across the entire to be analyzed for variants (Li, Li, &

Guan, 2008; Wang & Wang, 2009). The Affymetrix SNP (single nucleotide polymorphism) array 6.0 can investigate 1.8 million markers on a single microarray (Affymetrix Inc., Santa Clara, CA, USA). This type of microarray contains a large, high-quality platform that can be used to genotype an entire sample population (Wang & Wang, 2009). Single nucleotide polymorphisms are the most ideal marker for association studies because of their abundance. Roughly 12 million human SNPs have been uniquely assigned a reference number in the National Center

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for Biotechnology Information’s SNP database (Wheeler, Barrett, Benson,

Bryant, Canese, et al., 2008).

To date, there have been no genome-wide association studies of stuttering. Therefore, models for this type of study must come from research in other speech and language disorders. Specific Language

Impairment (SLI) has been intensively studied since the identification of

FOXP2.

FOXP2

The discovery of FOXP2 located on chromosome 7, by Hurst and colleagues (1990), has been pivotal in our identification and understanding of genes responsible for speech and language. The British family (KE family) from which a mutation in FOXP2 was identified, exhibited an autosomal dominant transmission (Mendelian model) of

Specific Language Impairment. Affected probands in the KE family exhibited oral-motor speech dyspraxia, low nonverbal IQ, and impaired expressive and receptive language abilities that were traced through three generations (Vargha-Khadem, Watkins, Alcock, Fletcher, &

Passingham, 1995). Since its initial discovery, many studies have further investigated FOXP2 (7q31) and FOXP2-regulated genes looking for an in- depth understanding of how this gene works and manifests its as Specific Language Impairment (SLI)(Lai, Fisher, Hurst, Vargha-

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Khadem, & Monaco, 2001; Spiteri, Konopka, Coppola, Bomar, Oldham,

Ou, et al., 2007; Vernes, Spiteri, Nicod, Groszer, Taylor, Davies, et al.

2007; Whitehouse, Barry, & Bishop, 2008). These related studies have uncovered overlap genes associated with autism, schizophrenia, Silver-

Russell Syndrome, and Alzheimer’s disease (Feuk, Carson, & Scherer,

2006; Whitehouse, Barry, & Bishop, 2008). While mutations in FOXP2 are found to be rare outside of the KE family (MacDermot, Bonora,

Sykes, Coupe, Lai, Vernes, et al. 2005), the discovery itself has provided scientists with a springboard to facilitate neurogenetic investigations of spoken language. Fisher and Scharff (2009) discuss the broader relevance of downstream effects of FOXP2, such as one found in a genetic screening that uncovered CNTNAP2 (contactin-associated -like 2), which binds to FOXP2. CNTNAP2 is a neurexin transmembrane protein that functions in neuron-glia interactions and is involved in cell adhesion and neuronal recognition (Inda, DeFelipe, &

Munoz, 2006; Vernes, Newbury, Abrahams, Winchester, Nicod, Groszer, et al. 2008). CNTNAP2 has been found to be involved in the development of the cerebral cortex (Lai, et al., 2003), specifically cortical layers (Vernes et al., 2008), and frontal grey matter development (Abrahams, Tentler,

Perederiy, Oldham, Coppola, & Geschwind, 2007), an area critical to speech and language. Interestingly, mutations of CNTNAP2 were found in a family exhibiting developmental malformations of their cerebral cortex, epilepsy, language disorders, and autistic characteristics (Strauss,

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Puffenberger, Huentelman, Gottlieb, Dobrin, Parod, et al, 2006).

Whereas this discovery does not directly affect our understanding of stuttering, it does provide an encouraging model. The discovery of

FOXP2 started with a very small study; 16 affected probands and 15 unaffected family members. Since its initial implications within the KE family, subsequent studies have led researchers to more and more information that is slowly piecing together our understanding of neurogenic contribution/involvement in language disorders.

Behavioral Genetics

The Environment

DNA is both inherited and environmentally responsive (Robinson,

2004a). “What genes actually do in the brain reflects the interaction between hereditary and environmental information” (Robinson, 2004b).

Many phenotypes relevant to aspects of human behavioral development are influenced not only by the environment, but also by multiple genes, and thus are referred to as ‘complex’. Stuttering has been identified by several researchers as a complex trait (Cox, Kramer, & Kidd, 1984; Kidd,

1979; Riaz et al., 2005)

Genetic variants that affect complex traits are neither necessary nor sufficient enough to cause a particular disorder on their own.

Independently, each variant has a small effect size and requires that

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many be present for an individual to be classified as affected with the disorder. When these genetic traits can be measured quantitatively, the genes with variation contributing to them are known as quantitative trait loci (QTL)(Eley & Craig, 2005). Since genetic studies for complex disorders must identify multiple genes responsible for the etiology, methods for identifying QTLs are very different than identifying genes for disorders with a single contributing gene (ie. Huntington ’s disease).

Quantitative genetic strategies facilitate measurement of genetic variation responsible for complex traits. “The strength of quantitative genetics is that it can examine the net effects of genetic and environmental influences on complex traits regardless of how many genes or environmental factors affect the traits and without knowing what those genes or environmental factors are” (Harlaar, Butcher,

Meaburn, Sham, Craig, & Plomin, 2005, p. 1097).

Taking into consideration the influential role the environment undoubtedly plays in the severity of stuttering, it is reasonable to assume that the environment may also play an interactive role with the development and genetics of stuttering. Continuing in the pursuit of definitive answers, quantitative genetic strategies are scientifically logical to pursue.

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Theories, Rationale, and Hypothesis

Theories

People differ greatly in all aspects of what is casually known as

speech fluency. A continuum of abilities and coordination exists not only

in fluent speakers, but in those classified with the disorder of stuttering

as well.2 Wide variability is often observed among affected individuals.

This variability manifests itself in frequency and duration of stutters,

type of stutters predominantly displayed, tension, secondaries, and age

at onset (among other characteristics), as well as trends toward

persistency or recovery. There is no wonder why decades of work

dedicated to the understanding of etiological factors, resulting in the

manifestation of such a disorder, would also produce results as varied

and complex as the disorder itself. “.. so far, not a single cause of

developmental stuttering has been unequivocally identified” (Yairi &

Ambrose, 2002, p. 10). “A number of epidemiological findings are

consistent with the notion of strong genetic influences in the etiology of

stuttering, but concerted efforts to find specific genes that account for

the susceptibility, and heredity patterns, are still in their infancy”

(Dworzynski et al., 2007, p. 169 ). An undeniable abundance of literature

has pointed to genetic underpinnings, yet the specifics remain elusive.

2 Unlike Bloodstein’s continuity hypothesis (1970), which suggests that stuttering moments and moments of normal disfluency lie on a continuum, this reference is to two distinct groups, those who do, and those who do not stutter. 32

Three potential theories may account for the evident variability:

1. Final Common Pathway Theory

Mellon, Umar, and Hanson (1993) theorize that perhaps dozens of gene variants lead to the phenotype of stuttering. They refer to this idea as the “final common pathway.” They propose that heterogeneity is paired with factors that reduce the penetrance of the phenotype through chaotic influences during development. Mellon and Clark (1990) use evidence of developmental plasticity to support their ideas.

Neurological studies published after the 1990s have built on the framework of developmental plasticity specifically under the realm of neuroplasticity. The evidence for neuroplasticity demonstrates the brain’s ability to repair, reorganize, recover and build from environmentally induced changes or demands occurring post natum.

Examples of trauma, learning, and pain rank among widely researched evidence for this phenomenon. Functional differences in people who stutter have been observed with fMRI, PET, and EGG studies (Ingham,

Fox, Ingham, Zamarripa, Martin, & Jerabek, 1996; Watkins, Smith,

Davis, & Howell, 2007). It remains unclear if these differences precede the onset of the disorder, or are themselves, examples of neuroplastic reorganization resulting from the experience of stuttering.

Learning induced neuroplasticity has been proven to be reflected

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even at the structural level. One study taught 24 adults to juggle, and compared them with age-gender-matched controls. Brain scans were performed prior to 3 months of juggling training for the experimental group. The researchers not only found functional changes in the brain, but also structural changes in the brain. They rescanned the groups after three months of no juggling, and found the structures had returned to near baseline measurements (Draganski, Gaser, Busch, Schuierer,

Bogdahn & May, 2004). This study demonstrates just how anatomically flexible the human brain is even within a relatively short period of time.

This phenomenon may help explain structural neuroanatomical differences observed in adults who stutter (Foundas, Bollich, Corey,

Hurley, & Heilman, 2001; Sommer, Koch, Paulus, Weiller, & Büchel,

2002) and in children who stutter (Chang, Erickson, Ambrose,

Hasegawa-Johnson, & Ludlow, 2007). One of the structural differences found among affected individuals is in the number and shape of cortical gyri. “Considering that gyrification is a complex developmental procedure, these findings indicate a developmental disorder (Büchel &

Sommer, 2004, p. 162).” The malleability of the brain is so important that a lack of neuroplasticity has been linked to diseases such as

Alzheimer’s (Mesulam, 1999).

Cognitive ability requires structural and functional cooperation of the brain. As deviations in structure and function are recorded, so is the variation among these deviations. Each study mentioned earlier found

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trends in functional and anatomical differences, but not one study found a single straight-forward consistent difference. Brain maturation requires years of nutritional development, environmental stimulation, and a sound genetic infrastructure (Plomin & DeFries, 1998). Genes specifically responsible for brain development and neurological function may underlie developmental stuttering, as these genes too are environmentally responsive during the critical stages of pre-natal and post-natal development. A balanced view, in which nature and nurture interact during this development in a final common pathway, could produce the variations observed in affected individuals.

2. Oligogenic Theory

In recent years, oligogenic traits have gained some attention.

Specifically, these are complex traits with a smaller number of contributing genes, one or more of which might be a major susceptibility locus, whose expression is influenced by the environment as well as other genes. For some oligogenic diseases, the major susceptibility loci may be different for different families. Genetic studies of stuttering are consistent with the possibility that it is an oligogenic trait.

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3. An Addiction Model

Scientists have reportedly found genes linked to addiction.

Whether these genes are specific to food addictions, substance abuse, or obsessive compulsive disorder, they all demonstrate incomplete penetrance and threshold characteristics that make someone more or less vulnerable to addictive behaviors. There is the presumption that for affected individuals a difficulty exists in their ability to execute a cessation of behaviors once they start. In light of the findings, the genetic pre-disposition for addiction does not doom the individual to inevitably become an addict, but rather that an interplay between the genes and the environment determines addiction risk.

Like many other behavioral diseases, addiction vulnerability is a complex trait (Enoch & Goldman, 1999). Several genes have been discovered through the use of animal models. See Table 2.8 for a summary of genes linked to alcohol and cocaine.

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Table 2.8 Genes associated with alcohol and drug addiction

Study Gene Function Goldman, Brown, The A1 allele of the dopamine receptor Albaugh, Robin gene DRD2 is more common in people Goodson, Trunzo addicted to alcohol or cocaine. DRD2 Akhtar, Derse, Long, Linnoila, &

Dean, 2006 Fehr, Grintschuk, Mice bred to lack the serotonin receptor Szegedi, gene Htr1b are more attracted to cocaine Anghelescu, and alcohol. Htr1b Klawe, Singer, Hiemke, & Dahmen, 2000 Epping-Jordan, Mice bred to lack the β2 subunit of Picciotto, Jean- nicotinic cholinergic receptors have a β2 Changeux & Pich, reduced reward response to cocaine. 1999 Thiele, Marsh, Mice with low levels of neuropeptide Y Marie, Berstein, & neurope drink more alcohol, whereas those with Palmiter, 1998 ptide Y higher levels tend to abstain.

Rothenfluh & Fruit flies mutated to be unable to Heberlein, 2002 synthesize tyramine remain sedate even tyramine after repeated doses of cocaine.

Spanagel, Mice mutated with a defective Per2 gene Pendyala, Abarca, drink three times more alcohol than Zghoul, Sanchis- Per2 normal. Segura, Magnone, et al., 2005 Crabb, Alcoholism is rare in people with two Edenberg, copies of the ALDH*2 gene variation. ALDH*2 Bosron, & Li, 1989

Modified from Genetic Science Learning Center, 2009

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Although we are unable to make mice stutter, or use other animal

models to investigate the genetics of developmental stuttering3, the mode of genetic integration in addiction makes an interesting analogy. Table

2.8 outlines several genes acting independently of each other, which all contribute to the same phenotypic behaviors and outcomes. When looking specifically at the genes, it can be seen that some are mutations, others are replication variations, while others are normal alleles found among both affected and unaffected individuals.

Stuttering has several potential similarities, especially if one examines the genetically instigated reward system. Noble, Blum and colleagues (1990) identified the D2 dopamine receptor (DRD2) polymorphism to contribute to risk of severe alcoholism. Later studies linked the A1 variation of the DRD2 gene to a large percentage of alcoholics, drug abusers, smokers, and other addictive, compulsive or impulsive disorders. This gene affects dopamine receptors, and positively reinforces repetitive behaviors in an individual regardless of external judgment of benefit. The likelihood of identifying specifically the role

DRD2 in people who stutter is quite low. This does not diminish, however, the potential of other shared genes with addiction disorders that may exist to contribute to our understanding of stuttering

3 Professor David Clayton at the University of Illinois has been able to simulate stuttering in Australian zebra finch songbirds (2006, personal communication). His work, along with that of and Kobayashi, Hiroyuki, and Okanoya (2001), (who were able to simulate stuttering in Bengalese finches) does not contribute to genetic or functional knowledge of developmental stuttering as the stuttering generated in these songbirds was lesion based. In both cases, lesions were made in the anterior forebrain of region X and resulted only in sound repetitions in the bird’s song. 38

behaviors.

Goals

The aim of this study is:

1) to identify replicable candidate genes that influence the

risk of individuals to stutter.

2) to investigate the pathways in which these genes

function to disrupt the fluent production of speech.

Rationale

One of the three models discussed earlier or any combination of the three models may potentially explain the underlying cause of stuttering. Elements of each model provide reasonable elements supporting the phenotypic characteristics and patterns of heritability currently observed in the disorder. Instead of pursuing a transmission model, which has in the past yielded inconclusive results, this study aims to identify specific genes contributing to the risk of stuttering. As exemplified in the discovery of FOXP2, a small discovery set of genes has the potential to lead researchers to concrete evidence of specific gene contribution/involvement and neuro-genetic relationships.

Presently there is no consensus regarding which transmission model, chromosomes, genes, alleles, or sex factors are involved in the

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expression of stuttering. A GWAS screens “without any prior predilection for specific regions, genes, or variants thereof” (Kitsios & Zintzaras, 2009, p. 161). In fact, GWAS have been referred to as a “hypothesis-free” approach (Hunter et al., 2008; Pearson & Manolio, 2008), making it an ideal candidate for this complex disorder.

Linkage studies are not as powerful as association studies for the identification of genes contributing to the risk for complex diseases

(Risch & Merikangas, 1996). A whole-genome case-control association study for genes involved in the disorder of stuttering is the next logical step in identification. To maximize the chance of success, subjects with clearly defined persistent phenotypes will be chosen for affected cases.

Hypothesis

Based on current literature and purposed theories mentioned earlier, three functional categories of genes are predicted:

1. Neural development

2. Neural function (e.g. transport genes or hormones)

3. Behavioral genes (specifically implicated phenotypically in addiction)

A study which produces replicable findings congruent with sound theory and evidence would be invaluable to the future of stuttering both clinically and scholarly. “Moreover, identification of genetic risk factors

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will aid in the design of epidemiological studies that might identify non- genetic risk factors for stuttering that could be targets for cost-effective prevention strategies” (Cox, 2004, personal communication).

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Chapter 3

Methods

Subjects

One hundred and twenty two experimental subjects ages 13 to 70 years were recruited from two primary sources. Seventy-six unrelated individuals of European descent already recruited in a previous linkage study of stuttering (Suresh, Ambrose, Roe, Pluzhnikov, Wittke-

Thompson, Ng, Wu, Cook, Lundstrom, Garsten, Ezrati, Yairi, & Cox,

2006) were combined with 46 new subjects recruited through the

University of Illinois Stuttering Research Program under a current NIH funded cross-sectional study of stuttering. One hundred and seven control subjects’ data were downloaded from www.HapMap.org. This database includes detailed genetic information for trios of family members, typically a mother, father, and child. Unrelated individuals

(mothers and fathers) were used. These subjects were coded CEU (CEU –

CEPH samples of European descent living in Utah, USA). A record of participants is listed in Table 3.1. It is unknown if any of these individuals are affected with stuttering. They represent a general population sampling and it is conceivable that one or two individuals in this sample would be affected/or recovered from the disorder of interest.

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Table 3.1 Demographic Data of Participants

Ethnic Included in N Sex Status Recruited Group GWAS Male: 29 European 35 Persistent UIUC Y Female: 6 Male: 31 Suresh et al., European 49 Persistent Y Female: 18 2006 European Male: 24 Suresh et al., 27 Persistent N (Israel) Female: 3 2006 European Male: 54 107 Control HapMap Y CEU Female: 53 African Male: 56 108 Control HapMap N YRI Female: 52 Asian Male: 54 105 Control HapMap N CHB+JPT Female: 51 African Male: 3 4 Persistent UIUC N American Female: 1 Male: 4 Asian 5 Persistent UIUC N Female: 1 Male: 2 Other 2 Persistent UIUC N Female: 0 *Some participants were not included in the final GWAS due to ethnicity, lack of matched controls, or failed quality control measures. Some samples included in this table from HapMap were used only for EIGENSTRAT analysis and ethnicity verification.

Selection Criteria: Diagnosis of Stuttering

For each potential participant, stuttering was verified by a co-PI of the grant or a highly specialized Speech-Language Pathologist with expertise in stuttering and related fluency disorders. Individuals identified for this study were required to meet one or more of the following criteria: (a) direct observation of stuttering by the investigators in person or via telephone, (b) diagnosis and/or treatment of stuttering

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by a speech-language pathologist or (c) detailed description of stuttering behaviors by the participant judged sufficient to positively identify stuttering. In addition, only participants with persistent stuttering were used. This required each subject to (a) self-report or (b) observably continue to exhibit stuttering behaviors.

This diagnostic criteria was also used in the Suresh et al. (2006) study to diagnose persistent developmental stuttering in those participants. Seventy-six participants from Suresh et al. (2006) were included in the present study, therefore, diagnostic standards among the combined participants were strictly maintained.

Data Collection/ Data Access

Individuals who stutter were recruited through the Illinois

International Stuttering Research Program, the University of Illinois

Speech Language Pathology Clinic, newspaper ads, and personal referrals of colleagues. These participants were given a preliminary stuttering diagnosis screening consisting of criteria listed above. Upon a confirmed diagnosis, 10 ml of blood was drawn at a medical clinic by a physician or phlebotomist from an antecubital vein into two 5 ml plastic vacutainer tubes or 2 ml of saliva was collected with an OrageneDNA Spit

Kit by the investigator. Control subject data are available to academic researchers in a web-based database (www.hapmap.org).

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Extraction

DNA from blood and saliva samples was extracted at the University of Chicago. Blood samples were stored in a -80°C freezer until sample collection was complete. Using a PureGene DNA extraction kit (Gentra,

Qiagen Inc., Valencia, California), blood samples were thawed, purified, and DNA was isolated for polymerase chain reaction (PCR) and genotyping. The PureGene cell kit enables purification of high-molecular- weight (100–200 kb) DNA to be suitable for high-density genotyping. The purification procedure removes enzyme inhibitors and contaminants from the sample improving the quality of the sample for more accurate genotyping results. PCR enables the production of millions of copies of specific DNA sequences. Once DNA is replicated, it can be applied to a microchip gene array for genotyping.

DNA from saliva samples were extracted using OrageneDNA extaction protocol for 4.0mL samples (Genotek Inc., Ottawa, Onterio,

Canada). Samples were stored at room temperature then were weighted and incubated in 50°C water bath for 2 hours before undergoing the purification and extraction process.

A NanoDrop spectrophotometer cuvette was used to ensure proper molecular weight of the extracted DNA from the saliva samples. This step verifies that the sample is suitable for downstream applications, including PCR and genotyping. Absorbance values (of light) at 260 nm

45

were accepted between 0.1 and 1.5. These values reflect those of good quality samples yielding an expected molecular concentration range of 20 to 200 ng/μL. All saliva samples passed this quality-control check.

Genotyping

All genotyping in the case samples was performed with the

Affymetrix 6.0 SNP chip which allows researchers assay up to 906,600 single nucleotide polymorphisms (SNPs) and more than 900,000 additional genomic features for assessment of copy number variants

(CNVs) across the human genome (Li, Li, & Guan, 2008). According to the manufacturer’s guidelines, we used 250 ng genomic DNA to genotype each sample. On day 1, samples are cut with a restriction enzyme and ligated to adapters. A generic primer is applied that recognizes the adapter sequence and selectively amplifies fragments within a certain size range. This reduces the complexity of the genome down to usable portions. On day 2, the amplified DNA is then fragmented, labeled, and hybridized to the array. Very little DNA is needed (250 ng) to assay a large number of SNPs with high call rates (>99%)( Korn, Kuruvilla,

McCarroll, Wysoker, Namesh, et al., 2008). Genotypes were called using the Birdseed genotype calling algorithm (Korn, et al., 2008).

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Quality Control

Initial quality control of the samples focused on identifying and

flagging poor quality data; we removed samples missing 10% or more

genotypes across all SNPs and SNPs missing in at least 10% of all

individuals. In addition, we removed samples with disproportionate (high

or low) heterozygosities relative to the distribution of heterozygosity

across all samples and flagged SNPs with highly significant departures

from Hardy Weinberg equilibrium. Previously unknown relatedness

between subjects was examined by looking at IBD (identidy by descent)

in all individuals considered pairwise. This procedure also indentified

any duplicated samples in the study.

The HapMap control samples and the previously collected samples

from Suresh et al. (2006) were from pools of related individuals.

Verification of reported sex was examined by looking at the inbreeding

coefficient F for the sex chromosomes of each individual in order to help

insure that the correct unrelated samples were being examined.

The statistical formula is based from:

Fst ~ (π0 – (π1+ π2)/2)/ π0)

Where π0 is the nucleotide diversity in the whole population and (π1+

π2)/2 is the average of the nucleotide diversity in the 2

subpopulations we are looking at. This is a general statistic to

population genetics but here it is used to determine sex. For a female

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with XX, they will have a nucleotide diversity roughly that of the whole population or autosome (π0), so F = 0 and in XY males they all look like homozygotes so (π1+ π2)/2 = 0 so F = 1.

Principal Components Analysis

Principal components analysis was performed using the

EIGENSTRAT software (Price, Patterson, Plenge, Weinblatt, Shadick, &

Reich, 2008). In initial analyses to identify potential population substructure, we included HapMap samples of European descent (CEU –

CEPH samples of European descent living in Utah, USA), African descent

(YRI – samples from Yoruba living in Ibadan, Nigeria), and Asian descent

(CHB and JPT – samples from Han Chinese living in Beijing, China and samples from Japanese living in Tokyo, Japan) along with those from our subjects who were ascertained for current or past stuttering. This initial analysis allows for the identification of individuals of recent European descent. The non-European samples were then removed once ancestry had been determined, and the European samples (including controls) were then analyzed using EIGENSTRAT software. The original QQ plot (a quantile-quantile (QQ) plot is a graphical technique for determining if two data sets come from populations with a common distribution) showed some deviations from the predicted standard regression line. We excluded the Israeli samples and reran EIGENSTRAT. The Israeli

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samples did not appear to be covariates, so they were excluded from the study. EIGENSTRAT was rerun using only samples of European descent and the resulting eigenvectors were used as covariates in the association studies to insure that results were not compromised by inappropriate matching of European descent cases and controls.

Data Analysis

A computer software program, PLINK (Purcell, Neale, Todd-Brown,

Thomas, Manuel, et al., 2007), was used for data analysis. Chi-square tests were performed to assess frequency differences between cases and controls. SNPs with p-values < 10-3 were annotated for function and any genes in Linkage Disequilibrium with the SNP4. These SNPs were annotated for any genes for which the SNP is an expression quantitative trait locus (eQTL). In other words, certain SNPs are known to predict the expression of a certain gene and are known as eQTLs.

Statistical Analysis

Primary Analysis

Significance of association between the disease phenotype and

4 Linkage Disequilibrium occurs when two genes are seen together more often than they should be if they were unlinked. This is a strong suggestion that the genes in question are not randomly associated with each other and may be implicated in the genotype of interest. 49

each SNP was assessed using logistical regression as implemented in

PLINK. Eigenvectors specific to individuals of European descent were used as covariates in the analysis. Individuals from Israel were removed from the analysis because QQ plots revealed an over-inflation of p-values even with correction for population stratification using eigenvectors as covariates.

Candidate-Gene Analysis

Candidate genes were selected via identification of regions demonstrating allele (or haplotype) frequency differences between the experimental and control groups. In addition, SNPs on the GeneChip array were distinguished with those that were identical to regions identified in previous studies as showing an association with stuttering.

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Chapter 4

Results

Principal Components Analysis

Principal components analyses were performed using EIGENSTRAT to detect population substructure. Four control HapMap populations,

CEU (European), CHB(Chinese), JPT(Japanese), and YRI(Yoruba), were used to genetically determine ancestry of affected stuttering individuals.

Substructure present in experimental samples of European ancestry caused two local clusters on the primary and second eigenvectors overlapping and neighboring the CEU controls (the samples are indicated in Figure 4.1) One cluster overlaps with the CEU controls (indicated in

Figure 4.1 by a red circle). Experimental samples clustering adjacent to the CEU control sample were confirmed to be of self reported Isreali descent.

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Figure 4.1 EIGENSTRAT Analysis of Ancestry

Several experimental subjects were observed to be of Asian,

African, and mixed ancestry. These subjects were removed from further statistical analysis and the GWAS to prevent possible false positive results due to population substructure.

Association Analysis

The original QQ plot of all European affected subjects (Isreali and western Eueropean) displayed a large dispersion from the expected p- values of the regression identity line (Figure 4.2).

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Figure 4.2 Original Quantile-Quantile Plot of European Ancestry

Deviation from the diagonal identity line suggests that either the assumed distribution is incorrect or that the sample contains values arising from true associations. The magnitude of this variation suggests that this dispersion is not due to true associations, but to significant differences in population structure or, in this case, an over-correction of p-values. An over-correction can be detected by observing p-values that deviate below the line toward the expected side. This indicates lower p- values on average than would be expected, suggesting that too many eigenvectors are being used as covariates and the resulting values are too conservative. The Israeli samples are likely very similar to the rest of the

European samples differing at only a small number of SNP sites. If the

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additional eigenvector is used to account for the small but significant ancestry differences, all SNPs are corrected based on these differences when most sites don’t need this correction. Hence, Israeli samples were removed (n = 27), thus correcting for ancestry variance and the corrected

QQ-plot (Figure 4.3) demonstrates an ideal plot with very little dispersion. Points observed outside of the gray area (the 95% confidence interval) are assumed to be associated with the disease of interest.

Figure 4.3 Corrected Quantile-Quantile Plot of Strict Northern European

Ancestry

Statistical Analysis

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PLINK software indentified 97 SNPs from the GWAS with p-values less than 10-3 significance in the stuttering population. Within this subset, SNPs with a p-value less than 10-4 and greater than 10-8 were considered (72 SNPs) per standard Bonferroni correction. Of these SNPs, only 22 contained known genes.

Table 4.1 Statistically Significant SNPs Showing Association with

Persistent Developmental Stuttering

Chromosome SNP BP p-value Gene/ function 1 rs4341397 56177988 2.85e-05 NA 1 rs1028400 206180354 3.08e-05 NA 1 rs1212304 206185202 4.76e-05 NA 1 rs10888965 56522669 5.98e-05 NA 1 rs778384 56409754 6.37e-05 NA 1 rs778417 56417155 6.37e-05 NA 1 rs12035224 206196311 6.99e-05 NA 1 rs4927315 56543494 7.20e-05 NA 1 rs2745967 206195345 7.73e-05 NA 1 rs16830063 198450969 1.16e-04 NA 2 rs10203329 223058245 1.15e-05 SGPP2 2 rs12052330 235832565 2.14e-05 NA 2 rs11781317 122351199 2.81e-05 NA 2 rs840950 65572319 4.88e-05 NA 2 rs6727792 53558145 6.40e-05 NA 2 rs7575607 31493959 6.57e-05 NA 2 rs840952 65573445 8.70e-05 NA 2 rs2573111 125682936 1.24e-04 NA 2 rs13028172 196440546 1.306e-04 DNAH7 3 rs7428796 86338362 9.06e-07 NA 3 rs524438 119602372 4.23e-05 NA 3 rs6809248 52284378 7.23e-05 WDR82 4 rs4861810 180938089 1.22e-05 NA 4 rs12331487 97711397 1.92e-05 NA 4 rs1585273 180938584 6.86e-05 NA 4 rs9884651 27575780 7.74e-05 NA 4 Rs10032173 27593263 1.12e-04 NA 4 Rs2019467 13902990 1.17e-04 NA 5 Rs10463397 147502740 1.71e-06 NA 5 Rs12655124 153802728 1.26e-04 NA 6 Rs4714735 44133677 3.04e-06 NA 6 Rs2326574 4986469 1.71e-05 LOC100129 6 Rs2994694 4980651 2.17e-05 LOC100129 6 Rs2793240 4948179 8.31e-05 RPP40 6 Rs795809 4965672 8.31e-05 NA 6 Rs2764114 4996182 1.25e-04 NA

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Table 4.1 (Cont.)

7 Rs10231824 131572872 2.94e-05 PLXNA4 7 Rs10254737 131583166 4.82e-05 PLXNA4 7 Rs6944400 131583025 6.98e-05 PLXNA4 7 Rs6467426 131561969 1.21e-04 PLXNA4 7 Rs1349406 131558291 1.29e-04 PLXNA4 8 Rs17517144 18377514 1.30e-05 NA 8 Rs6577921 139491434 4.09e-05 FAM135B 8 Rs7828809 139492089 4.09e-05 FAM135B 8 Rs17517777 18468696 8.70e-05 PSD3 8 Rs17596083 18468372 1.14e-04 PSD3 8 Rs10503616 18463721 1.30e-04 PSD3 9 Rs4744783 77971147 1.18e-05 PCSK5 9 Rs1411956 77967757 3.70e-05 PCSK5 9 Rs3903907 114764370 6.10e-05 LOC100129 9 Rs4978523 114763299 1.00e-04 LOC100129 9 Rs1330042 117153667 1.15e-04 NA 10 Rs17114000 90645089 2.31e-07 STAMBPL1 10 Rs10996890 67522056 2.52e-05 CTNNA3 10 Rs10996884 67515943 6.62e-05 CTNNA3 10 Rs10736957 305525 1.05e-04 NA 10 Rs17156343 1468227 1.29e-04 ADARB2 11 Rs7119667 61391128 2.23e-06 FADS2 11 Rs562028 59634426 9.87e-05 NA 11 Rs10897020 59794143 6.05e-05 NA 12 Rs11104852 87318456 3.12e-06 NA 12 Rs11169953 50590666 4.75e-05 ACVRL1 12 Rs4964826 103539674 2.45e-05 CHST11 12 Rs10507177 103533038 8.90e-05 CHST11 13 Rs9600564 75333636 9.60e-06 NA 13 Rs12584838 27109308 8.01e-05 POLR1D 14 Rs10133907 21689758 5.26e-05 NA 14 Rs9323108 21723598 9.10e-05 NA 14 Rs2415328 35958283 1.14e-04 NA 15 Rs11072923 78587211 5.20e-05 ARNT2 15 Rs12148477 49440411 6.21e-05 GLDN 15 Rs12593365 30895282 1.11e-04 FMN1 16 Rs1566184 74517245 3.73e-05 NA 17 Rs4306569 29853048 1.26e-04 NA 18 Rs6508606 25465343 2.64e-05 NA 18 Rs8097651 25486009 1.03e-04 NA 19 Rs3764557 15731246 9.04e-05 NA 20 Rs6094607 45206827 1.38e-05 EYA2 20 Rs6081636 19474066 2.11e-05 SLC24A3 20 Rs13045300 19745047 3.54e-05 NA 20 Rs6037460 304785 4.35e-05 NA 20 Rs1008031 45226938 5.13e-05 EYA2 20 Rs6051550 305570 6.10e-05 NA 20 Rs6139982 6359560 8.75e-05 NA 20 Rs4810619 45218428 1.04e-04 EYA2 20 Rs6085561 6377864 1.12e-04 NA 20 Rs6066218 45224368 1.14e-04 EYA2 20 Rs3790224 19405878 1.24e-04 SLC24A3 23 Rs2335791 35586481 7.94e-05 NA 23 Rs5928966 35602838 8.42e-05 NA 23 Rs5970769 23573835 1.25e-04 NA

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Associations were plotted as –log10 P values for GWAS in 84 cases with developmental stuttering and 107 controls, showing single data points for SNPs with P<10-4 (horizontal red line) for 22 autosomes and the X chromosome. SNPs between the lower limit of P<10-4 and the upper limit of P>10-7 were considered for candidate gene analysis.

Figure 4.4 Manhattan Plot of SNPs

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Candidate-Gene Analysis

Candidate genes were selected among the SNPs demonstrating haplotype frequency differences between the affected and control samples within the selected p-value criteria. SNPs were filtered for minor allele frequency <0.01 (MAF), genotype call rate <0.05, and Hardy-

Weinberg<10e-8. SNPs are filtered out for very low minor allele frequencies of less than 1% for two reasons: 1) there is very little power to detect an association with a low MAF SNP and 2) because genotyping calls are not as reliable for low frequency alleles and may be due to a genotyping error. The genotype call rate is filtered to only look at SNPs where at least 95% of the genotypes included that SNP. If a SNP gets “no call” more than 5% of the time in subjects, it doesn’t lend itself to genotyping reliability and may not have been correct when it was called.

The Hardy-Weinberg filter checks if SNPs are significantly out of Hardy-

Weinberg equilibrium (HWE). SNPs out of HWE are more likely to be spurious and/or a genotyping error than a true association. Candidate gene results are listed below in Table 4.2.

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Table 4.2 Candidate Genes/SNPs in association with persistent

developmental stuttering

Chro Gene moso SNP BP p-value function me fatty acid FADS2 11 rs7119667 61391128 2.23e-06 desaturase 2 plexin PLXNA4 7 rs10231824 131572872 2.94e-05 A4 catenin (cadherin- CTNNA3 10 rs10996890 67522056 2.52e-05 associated protein), alpha 3 aryl-hydrocarbon ARNT2 15 rs11072922 78587211 5.20e-05 receptor nuclear translocator 2 eyes absent homolog 2 (named for its EYA2 20 rs6094607 45206827 1.38e-05 initial discovery/functio n in Drosophila) proprotein PCSK5 9 rs4744783 77971147 1.18e-05 convertase subtilisin solute carrier family 24 (sodium/potassi SLC24A 20 rs6081636 19474066 2.11e-05 um/ calcium exchanger N/A N/A may be 2 rs2573111 125682936 1.02e-04 associated with CNTNAP5 forming FMN1 15 rs12593365 30895282 1.11e-04 1 adenosine ADARB2 10 rs17156343 1468227 1.29e-04 deaminase, RNA- specific, B2

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Figure 4.5 Manhattan Plot of Candidate Genes/SNPs in Association with

Persistent Developmental Stuttering

In addition, SNPs on the GeneChip array were distinguished with those that were identical to regions identified in previous studies as showing an association with stuttering.

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Table 4.3 GWAS overlap with previous studies

Most Important Other Potential GWAS Study Chromosome Chromosomes match Shugart et rs2573111, al., 2004 18 1, 2, 10, and 13 ADARB2, CTNNA3 Riaz et al., 2005 12 1, 5, 7, 12 PLXNA4

Male specific on PCSK5, ARNT2, Suresh et 9, 15, and 7, Female PLXNA4, al., 2006 conditionally 2 specific on 21 rs2573111 Wittke- Thompson 13 1, 3, 5, 9, 13, 15 PCSK5, ARNT2 et al., 2007

*Highlighted chromosomes represent candidates for potential genetic overlap.

On Chromosome 7, Suresh et al. (2006) reported LOD significance for CLCN1 which overlaps by cytogenetic region with PLXNA4 at 7q32.3.

Riaz et al. (2005) also reported a region on chromosome 7 at 7q36.3. This region does not overlap with any regions in the current study, but was found to regulate PLXNA3 (not PLXNA4 as identified in the GWAS, but in the same family) and ARNT genes. Unfortunately, several of the linkage studies did not provide enough information regarding the location of their chromosomal regions of interest. Overlap with other speech and language disorders, addiction disorders, neurological disorders was found to be associated with several of the genes found to be significant in the current study. See Table 4.4 below.

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Table 4.4 Overlap genes with other disorders

Chromo Gene Function Overlap Study some Desaturase enzymes regulate unsaturation of fatty acids through the Brookes, Chen, FADS2 11 introduction of double ADHD Xu, Taylor, & bonds between defined Asherson, 2006 carbons of the fatty acyl chain. Maestrini, Pagnamenta, Lamb, Bacchelli, PLXNA Inhibitory signaling Autism, Sykes, Sousa, et 7 molecule in the brain 4 Obesity al., 2009; Laramie & Matthew, 2008 Morgan, Hamilton, Turic, Jehu, Harold, Late-onset Abraham, et al., CTNNA 2008; 10 N/A Alzheimer’s 3 Miyashita, Arai, disease Asada, Imagawa, Matsubara, Shoji, Higuchi, et al., 2007 A transcription factor that acts as a partner Chakrabarti, for several sensor Dudbridge, Kent, proteins in the bHLH- Autism Wheelwright, PAS family. Primary ARNT2 15 Hill-Cawthorne, function in genes Allison, et al., responsive to 2009 developmental and environmental stimuli. The encoded protein Joslyn, may be post- SLI, Ravindranathan, translationally modified EYA2 20 Brush, Schuckit, and may play a role in Alcoholism & White, 2010 eye development. processes sugars in the PCSK5 9 brain, and predicts N/A N/A expression of SLC24A3 SLC24 transporter signaling 20 N/A N/A A3 molecule in the brain Kitahara, Kawa, Katsuyama, Umemura, N/A FMN1 15 Alcoholism Ozaki, Takayama, et al., 2008

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Table 4.4 Overlap genes with other disorders (cont.)

Gene Chromosome Function Overlap Study This gene encodes a member of the double- Grupe, Li, stranded RNA Rowland, adenosine Late- onset Nowotny, ADARB2 10 deaminase Alzheimer’s Hinrichs, family of RNA- disease Smemo, editing Kauwe, et al., enzymes and 2006 may play a regulatory role in RNA editing

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Chapter 5

Discussion

A GWAS was performed investigating 906,600 SNPs in 84

Caucasian northern European subjects characterized for their diagnosis of persistent developmental stuttering and 107 matched controls who do not stutter. As stated previously in the methodology, a systems biology analysis focusing on the functional categories of significant SNPs was used to approach the data. Rather than focus on the individual

SNPs/genes, evidence to support functionally related sets of genes was sought to determine candidate genes contributing to the pathology of persistent developmental stuttering. This was tested by individually investigating each statistically significant SNP for function and cross-SNP prediction or regulation. To perform this analysis, 97 SNPs were placed in a ranked list sorted from lowest to highest p-values according to association p-values within the desired confidence interval. Candidate genes were classified into three predicted functional categories: neural development, neural function (e.g. transport genes or hormones), and behavioral genes (specifically implicated phenotypically in addiction (see

Table 5.1)).

It should be noted that several candidate genes fit into more than one functional category. For example FMN1 plays a large role in brain

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development but has also been linked to alcoholism.

Table 5.1 Functional categories of candidate genes

Category Gene Function

Neural Development FADS2 Metabolizes fatty acids in the brain during development

CTNNA3 Mediates strong cell-cell adhesion EYA2 Regulates transcription during critical stages of embryonic development FMN1 Cytoskeletal formation, cell migration, and gene expression ARNT2 Regulates fetal neurogenesis Neural Function PLXNA4 Inhibitory signally molecule

PCSK5 Processes sugars

SLC24A Signaling molecule

ADARB2 Changes amino acid sequences predicted by genes

ARNT2 Regulates neuroendocrine system Behavioral Function FMN1 Functions in neural development, behavioral implications in Alcoholism

EYA2 Functions in neural development, behavioral implications in Alcoholism

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Neural Developmental Genes

Fatty acid desaturase 2 (FADS2, rs7119667) is located on the long

arm of chromosome 11 (11q12-q13.1) and comprise 39.113 nucleic

bases. FADS2 was found in this study to be significant at p=2.23e-06 , but has not been indentified in previous linkage studies of stuttering.

Fatty acids have been found to “affect behavior and cognition both directly and indirectly” (Brookes, Chen, Xiaohui, Taylor & Asherson,

2006, p. 1053). The human brain composition is approximately 60% lipid

(Settle, 2002). According to Hallahan and Garland (2005), fatty acids play a key role in modulating neural function and development as well as cognitive and behavioral development. Brookes et al. (2006) found FADS2 to be significantly associated with ADHD. As in addiction disorders,

ADHD is thought to be altered by the amount of available dopamine at the synapse (Goldman et al., 2006). Interestingly, some studies conducted on the neuronal response of dietary fatty acids have shown resulting alterations in dopamine neurotransmission (Brookes et al.,

2006). Investigations of individuals deficient in omega-3 fatty acids have reported a reduction in dopamine concentrations in the prefrontal cortex

(Delion, Chalon, Guilloteau, Besnard, & Durand, 1996; Zimmer,

Vancassel, Cantagrel, Brenton, Delamanche, Guilloteau, et al. (2002).

Animal studies of ADHD and fatty acids have confirmed this observation

(Russell, Villiers, Sagvolden, Lamm, & Taljaard, 1995; Sagvolden, 2000).

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Several studies have linked brain development and intelligence to the environmental exposure to fatty acids in breast milk (Anderson,

Johnstone, Remley, 1999; Caspi, Williams, Kim-Cohen, Craig, Milne,

Poulton, et al., 2007; Mortensen, Michaelson, Sanders, & Reinisch,

2002). Caspi and colleagues (2007) found that the association between breastfeeding and intelligence was solely moderated by FADS2. The study ruled out alternative explanations including exposure, intrauterine growth, social class, maternal cognitive ability and genetic differences in breast milk composition. The role FADS2 plays in modifying dietary fatty acids is substantial for brain development. The gene is the rate-limiting step in the metabolic pathway of fatty acid modification into arachidonic acid and docosahexaenoic acid, better known as DHA. Substantial amounts of these two compounds are produced in the human brain during the first few months of development following birth (Heird &

Lapillonne, 2005). Evidence supports the observation of children with higher levels of DHA and arachidonic acid to have enhanced cognitive development through early childhood (McCann & Ames, 2005) that produces cognitive benefits into adulthood as longitudinally reported by

IQ scores (Mortensen, et al., 2002).

A study investigating breastfeeding in children who stutter was recently conducted at the University of Illinois by Smith and Ambrose

(2009). A questionnaire was administered to mothers of children who stutter regarding breastfeeding initiation, exclusivity, and duration. The

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results supported the hypothesis that breastfeeding provides a measure of protection against persistent developmental stuttering. The role fatty acids play in regulating gene expression by altering the availability of transcription factors was highlighted (Jump, 2004; Oddy, 2006). In addition, authors suggested that an underlying mechanism of stuttering may be attributed to the differences observed in the neural tissue of formula-fed infants. Formula fed infants have a higher ratio of fatty acids that are not broken down into usable forms of DHA and may be associated with subtle impairments in neurotransmission, cell-to-cell signaling, and synaptic membrane function (Oddy, 2006).

The role of FADS2 in stuttering is not explicitly evident, however, it’s impact on brain development and function is undeniable. FADS2 appears to be very active in brain development through the early childhood years which correlates to stuttering onset, and for most children natural recovery. The evidence of FADS2s influence on cognition, behavior and dopamine transport paired with its involvement in DHA production (known to improve memory and learning processes) into adulthood would support potential involvement in adults with persistent developmental stuttering.

Neural Function Genes

PCSK5 is a pro-protein convertase subtilisin located medially on

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the long arm of chromosome 9 (rs4744783, 9q21.3) consisting of 302,724 nucleic bases. It is known to process glycoproteins in the brain (Cao,

Mok, Miskie, & Hegele, 2001). Glycoproteins act as hormones, enzymes, carriers, and/or inhibitors, among other roles. Not much is known about the specific function of this gene or its interaction with sugars in the brain, however, it does regulate the expression of SLC24A3 another candidate gene reported in this study.

SLC24A3 is a member of the solute carrier family and is located on the short arm of chromosome 20 (rs6081636, 20p13). It is composed of

562,256 nucleic bases and functions as a signaling molecule in the brain in sodium, potassium, and calcium exchange. Calcium and sodium exchangers are an integral component of intracellular homeostasis and the electrical conduction of neurons. According to Yang, Lee, Yoo, Choi, and Jeung (2009), potassium-dependent sodium/calcium exchangers, such as SLC24A3, functionally are believed to exchange 1 intracellular calcium and 1 potassium ion for the transport of 4 extracellular sodium ions in humans and mice. In mice SLC24A3 is regulated by the steroid hormones estrogen and progesterone (Yang, et al. 2009).

Chromosome 20 or SLC24A3 has never been identified in previous linkage studies of stuttering. However, another member of the solute carrier family (SLC6A3) was identified in stuttering populations among the Han Chinese (Lan, Song, Pan, Zhuang, Wang, Ma, et al., 2009).

SLC6A is located on the distal end of chromosome 5 (5p15.3) and not

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proximally related to SLC24A3.

Behavioral Genes

Three significant SNPs (PLXNA4, EYA2 and FMN1) for stuttering risk have also been found in gene regions specifically implicated in addiction disorders. Addiction disorders are interrelated by common neurobiological pathways, including those that regulate reward, anxiety, stress response, and behavioral control (Goldman, Oroszi, & Ducci,

2006). Addictions may include non-substance related behaviors such as gambling, playing video games and over-eating. It is hypothesized that certain repetitive behaviors might access the same neurobiological pathways that modulate impulsive behaviors, compulsive behaviors, mood and reward. These same pathways are hypothesized to positively reinforce repetitive behaviors in an individual regardless of external judgment of benefit. There is the presumption that for affected individuals a difficulty exists in their ability to execute a cessation of behaviors once they start; a notable comparison to stuttering behaviors.

PLXNA4 (7q32.3) has been strongly associated with obesity by several genetic linkage and SNP association studies (see a review by

Laramie, 2008). Work by Laramie (2008) identified several SNPs located in the gene PLXNA4 that were consistently replicated in other obesity samples. In general, the gene PLXNA4 repeatedly demonstrated a

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significant increase in linkage disequilibrium in obese populations.

Further evidence was sought for the influence of PLXNA4 in BMI (Body

Mass Index, rated over 30 for obese individuals). A candidate gene analysis of PLXNA4 revealed a large deletion on chromosome 7 in the region of 7q32 in a subset of the study population. This deletion was examined for its association with obesity related phenotypes including

BMI among others. Laramie (2008) reported evidence supporting the role of PLXNA4 not only in obesity and BMI, but also in type II diabetes.

Individuals who were homozygous deletion carriers had a 58% reduced risk of developing type II diabetes.

Two SNPs (EYA2 and FMN1) were found in gene regions specifically implicated in alcoholism. EYA2 (rs6094607, 20q13.1), located on the long arm of human chromosome 20, spans about 293,984 nucleic bases and was first discovered in drosophila as the gene responsible for eye development (Bonini, Bui, Gray-Board, & Warrick, 1997). Borsani,

DeGrandi, Ballanio, Bulfone, Bernard, Banfi, Gattuso et al. (1999) identified this gene as a member of the eyes absent (EYA) family of proteins in humans. In humans, however, it reportedly works together with SIX1 and DACH2 in myogenesis (Abdelhak, Kalatzis, Heilig,

Compain, Samson, Vincent, et al., 1997). Myogenesis is the formation of muscular tissue during embryonic development. It is also known to interact with GNAZ and GNAI2 to prevent nuclear translocation and transcriptional activity during fetal development (Ho & Wong, 2001).

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Embry, Glick, Linder, and Casey (2004) performed a GWAS on individuals diagnosed with an alcohol use disorders. EYA2 was among

172 other significant genes identified in a cohort of 367 affected sibling pairs. The study emphasized the magnitude of their findings was not in specific genes, but in genes that fit into functional categories, much like the current study.

EYA2 has also been found recently in association with Specific

Language Impairment (Tomblin, Evans, & Cox, 2010, personal communication). These findings have not yet been published, and like stuttering, the involvement of EYA2 in speech and language disorders remains puzzling. Perhaps some pathogenesis of EYA2 in muscular tissue formation during fetal development contributes to the breakdown of muscular control and speech fluency in vivo.

FMN1 (rs12593365), located medially on the long arm of chromosome 15 (15q13.3) consisting of 293,741 nucleic bases, has no previous association with stuttering. FMN1 was linked to alcoholism in

2008, by Kitahara, Kawa, Katsuyama, Umemura, Ozaki, and Takayama, as being a top candidate gene for susceptibility to alcoholic chronic pancreatitis as investigated in a GWAS of 65 Japanese individuals diagnosed with the condition. This search revealed 10 candidate susceptibility regions and 5 candidate resistant regions throughout the investigated population genome. FMN1 was the top candidate pick for susceptibility to alcoholism in this population. Much of what FMN1 is

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known to do is related to cellular adhesion and cellular structure. FMN1 contributes to the formation of adherens junctions (these are protein complexes that occur at cell-cell junctions in epithelial tissues) and is critically important in the polymerization of linear actin filaments that provide mechanical strength to cells (Zigmond, 2004). FMN1 interacts with alpha-catenin (a linking protein between cadherins (adhesion molecules that are calcium dependent) and actin-containing filaments) and may interact with tubulin (a globular protein that makes up mictrotubules) in fetal development (Zigmond, 2004).

Cross-Involvement

A different categorical differentiation offers some striking similarities for other disorders associated with the same candidate genes investigated in this study.

Table 5.2 Genetic overlap of candidate genes with other disorders

Gene Overlap

PLXNA4, ARNT2 Autism

EYA2, FMN1 Alcoholism

ADARB2, CTNNA3 Alzheimer’s disease

Genes implicated in additional disorders, such as alcoholism, were predicted, but the significance of overlap with autism and Alzheimer’s

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disease was not anticipated and associations between the disorder of interest and these disorders remain unclear.

Autism

Plexin A4 is a protein that works with semaphoring-3A in the brain to elicit inhibitory signals into cells and neurons (Laramie, 2008) PLXNA4

(rs10231824) is located on the long arm of chromosome 7 (7q32.3), that is comprised of 874,817 nucleic bases. It was found in this association to be significant at p=2.94e-05. This gene is located on a region of chromosome 7 implicated in a linkage study by Suresh et al. (2006).

Another region on chromosome 7 reported by Riaz et al. (2005) nearby at

7q36.3 was found to regulate PLXNA3, not PLXNA4 as identified in the

GWAS, but a gene closely related and functionally in the same family of genes.

PLXNA4 has recently been replicated in association with autism.

Maestrini, Pagnamenta, Lamb, Bacchelli, Sykes, Sousa, et al. (2009) performed a high-density association analysis on chromosome 7

(previously implicated in autism by linkage studies in The International

Molecular Genetic Study of Autism Consortium) of 253 families with children diagnosed with autism. The investigators used two approaches, a case-control and a family-based analysis. One of the strongest signals in the family-based analysis was PLXNA4.

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Outside of autism, PLXNA4 and its co-receptor neuropilin-1 (a protein involved in axon guidance, cell survival, migration, and invasion) were found to be expressed in several vocal control areas in avian brains

(Matsunaga & Okanoya, 2009). Specifically this gene showed very strong association among two different families of vocal learners, the Bengalese finch (Lonchura striata domestica), a songbird, and the budgerigar

(Melopsittacus undulatus), a parrot. These results suggest the possibility that the expression of PLXNA4 (in combination with semaphorin and neuropilin) is involved in the acquisition of vocal learning ability.

ARNT2 (rs11072921) is the other candidate gene also implicated in autism. ARNT2 is made of 193,578 nucleic bases and is located on the long arm of chromosome 15 (15q24). It is a member of bHLH-PAS transcription factor family that is involved in the regulation of several important pathways, including toxin metabolism, neurogenesis, hypoxic response, biorhythms, and the formation of the trachea (Crews, 1998;

Schmidt & Bradfield, 1996). This gene is specifically involved in the development of the neuroendocrine cells in the hypothalamus (Michaud,

DeRossi, May, Holdener, & Fan, 2000). Chakrabarti, Dudbridge, Kent,

Wheelwright, Hill-Cawthorne, Allison, et al. (2009) investigated 174 “high functioning” individuals diagnosed with autism, autistic traits, or

Asperger syndrome. ARNT2 plays a role in brain development and may contribute to the abnormal neural connectivity that has been proposed to underlie autism spectrum disorders (Belmonte, Allen, Beckel-Mitchener,

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Boulanger, Carper, & Webb, 2004). Two SNPs in the ARNT2 gene were found to be highly associated with the affected group. “These findings point to a key role played by these neurodevelopmental genes in the development of autistic traits” (Chakrabarti et al., 2009, p. 173).

ARNT2 has not been identified in any other genetic studies of stuttering. Riaz et al. (2005) identified a region (7q36.3) in a linkage study that regulates ARNT genes, however, a direct connection to ARNT2 has not been studied at the molecular level. Assumptions as to the involvement of ARNT2 in stuttering remain limited to its potential role in neurogenesis or its contribution to the development of the neuro- endocrine system that regulates stress response.

Alzheimer’s disease

CTNNA3 and ADARB2 have both been independently associated with late-onset Alzheimer’s disease and are both located on chromosome

10. CTNNA3, known formally as catenin alpha 3, is located on the long arm of (rs10996890, 10q21.3) and consists of 2,105,991 nucleic bases. It is thought to be involved in the formation of stretch- resistant cell-cell adhesion complexes (Janssens, Goossens, Staes,

Gilbert, van Hengel, Colpaert., et al., 2001).

Thus far, CTNNA3 has not been previously implicated in stuttering risk, however, regions on chromosome 10 were identified by Shugart et

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al. (2004). Overlap of these regions with CTNNA3 are possible as location information was not provided in the referenced study.

CTNNA3 is best known for its association with late-onset Alzheimer’s disease (Bertram, Blacker, Mullin, Keeney, Jones, Basu, et al., 2000;

Ertekin-Taner, Graff-Radford, Younkin, Eckman, Baker, Adamson,

Ronald, et al., 2000; Myers, Holmans, Marshall, Kwon, Meyer, Ramic, et al., 2000). Myers et al. (2000) reported that CTNNA3 is an uncommonly large gene spanning over 1.7Mb and covering several SNP regions. Within

CTNNA3 are two smaller significant gene regions that have also been implicated in Alzheimer’s disease. One of these genes is an encoding insulin degrading enzyme (IDE) and has been shown to be independently significant from CTNNA3 in Alzheimer’s disease association studies

(Prince, Feuk, Gu, Johansson, Gatz, Blennow, et al., 2003). Insulin and insulin-like signaling pathways have been investigated in animal models.

Components of these pathways were found to be involved not in

Alzheimer’s disease suceptibility, but in the regulation of lifespan (Hong,

Reynolds, Gatz, Johansson, Palmer, Gu, et al., 2008). Until recently, the relevance of those findings in human insulin pathways has remained obscure. In 2008, Hong and colleagues found evidence that IDE, the insulin degrading enzyme (the gene found within the gene region of

CTNNA3) may be influencing human life expectancy. Replications of this study have shown changes in IDE to be related to advancing age in men.

Thus suggesting that insulin levels are only associated with IDE in males

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(Hong, et al., 2008).

IDE is one of the central regulators of insulin metabolism, but also plays and integral role in the degrading of a variety of sugar proteins such as IGF2 and β-amyloid (Hong et al., 2008). Not only has this gene been associated with Alzheimer’s disease and life expectancy, but also with type II diabetes (Karamohamed, Demissie, Volchak, Liu, Heard-

Costa, Liu, et al., 2003). This gene, like PCSK5 (discussed later), plays an integral role in processing sugars in the brain. The role of sugar transport and stuttering have yet to be investigated at the molecular level, but remains an area of intrigue.

Another expression of CTNNA3 in the brain functions to bind itself to β-catenin and acts as a negative regulator of the Wnt-signalling cascade (Busby, Goossens, Nowotny, Hamilton, Smemo, Harold, et al.,

2004). The Wnt-signaling cascade is required for several crucial steps during early embryogenesis, and its activity is modulated by various agonists and antagonists to provide spatiotemporal-specific signaling

(Creyghton, Roel, Eichhorn, Vredeveld, Destree, & Bernards, 2006).

ADARB2 is located at the very end of the short arm on chromosome 10 (10p15.3) and consists of 561,598 nucleic bases. This gene encodes a member of the double-stranded RNA family of RNA-editing enzymes and is thought to play a regulatory role in RNA editing (Mittaz, Antonarakis, Huguchi, & Scott,

1997). ADARB2 was found in association with late-onset Alzheimer’s

78

disease in a population of 422 affected cases from Washington University

Alzheimer’s Disease Research Center patient registry, but was not significantly replicated in the same study investigating the association of multiple candidate gene regions in 368 cases from the UK and from 217 additional cases collected through the University of California–San Diego

(Grupe, Li, Rowland, Nowotny, Hinrichs, Smemo, et al., 2006).

The functional contribution of ADARB2 to Alzheimer’s disease has not yet been investigated, however, ADARB2 has been found to edit the mRNA of GluR2 (L-glutamate receptor) in normally functioning adults

(Kawahara, Ito, Sun, Kanazawa, & Kwak, 2003). L-glutamate is the primary excitatory neurotransmitter in the central nervous system

(Meldrum, 2000). Glutamate neurotransmission is involved in most aspects of normal brain function and is disrupted in many neuropathologic conditions such as Huntington's disease, spino- cerebellar degeneration syndromes, and motor neuron diseases (Bittigau

& Ikonomidou, 1997). GluR2, a glutamate receptor, is critically regulated by ADARB2 in the human brain. Kawahara et al. (2003) found that an abundance of ADARB2 inhibited the amount of GluR2 present in white matter. This discovery may lead to an understanding of deficient glutamate transmission as the mechanism underlying neurological diseases and eventual neuron death.

Differences in white matter have been found in the brains of people who stutter. Chang, Erickson, Ambrose, Hasegawa-Johnson, and Ludlow

79

(2008) found a decrease in white matter tracts underlying the oral facial motor regions of the left hemisphere in children who persistently stutter.

A study investigating white matter tracts in adults found right- hemispheric increases in white matter volume in several brain regions critical to speech and language production and face and mouth control.

These adults also lacked the normal left–right asymmetry in white matter volume in the auditory cortex (Jancke, Hanggi, & Steinmetz, 2004). In a study by Choo, Kraft, Loucks, Ambrose, Olivero, Sharma, et al. (2010, in review) the posterior region of the corpus collosum (a white matter tract connecting the right and left hemispheres of the brain) was found to be significantly thinner in adults who persistently stutter than controls.

Considering that ADARB2 and GluR2 are significantly involved in white matter and motor neuron pathology, it is interesting to note that motor stability (Kleinow & Smith, 2000)and motor-muscle movement initiation

(Salmelin, Schnitzler, Schmitz & Freund, 2000) is observed to be aberrant in people who stutter leading to a potentially interesting functional connection between ADARB2 and persistent developmental stuttering.

Study Limitations:

This discussion remains speculative at this stage. Ninety-seven

SNPs were statistically significant for association with stuttering in this

80

study. While this data suggests that there is an excess of associated

SNPs when compared to random expectations, there is no strong evidence implicating a specific marker, SNP, or gene among these. The lack of strong statistical support for an individual marker in this experiment is not unusual in the context of recent reports of GWAS experiments looking at other genetically complex diseases. Thus ten of the top SNPs containing known genes were analytically investigated as candidate regions for stuttering risk.

Significant limitations of this study result from a fairly small sample population. While most studies investigating the genetics of stuttering have historically been very small, typical discovery phase

GWAS studies require a sample size of approximately 400 cases and controls to draw strong statistical assumptions regarding association with disease risk (Pearson & Monolio, 2008).

Despite this limitation, this study has succeeded in demonstrating association in the set of candidate genes chosen, significantly higher than would be expected from a random selection of genes. This is important because this study was designed to be an exploratory whole- genome scan, rather than a testing multiple strong candidates previously identified in other studies. Each candidate SNP had fairly strong p-values reflecting a strong association with stuttering in this population. It is therefore reasonable to report these genes with genome-wide significance. In this study, only 10 candidate genes were discussed in

81

detail, this was due to the lack of functional information available for most SNP regions. Ninety-seven SNPs in this study reached nominal significance, with only 22 containing known genes. It is suggested that all such SNPs/genes are plausible targets for further replication whether functional information is currently available.

Conclusions:

In this study, 10 nominally significant candidate genes were assessed for functional significance to persistent developmental stuttering, some of which are also associated with autism, alcoholism, and Alzheimer’s disease. These 10 genes fall into the three functional categories related to neural development, neural function, and behavior, providing some support for the three purposed theories of stuttering transmission and genetic influence.

Future Directions

These initial associations are being followed up with fine-mapping of functional and causal variants, which may reveal genetic mechanisms with more precision. There is a strong need to conduct a large-scale replication study on this set of significant SNPs in a population sample with persistent developmental stuttering. Future studies will test these

82

genes in expression studies, as well as in individuals of diverse ancestry and in those who have naturally recovered to establish which combination of common SNPs (which individually are non-pathological) are common to the etiology of anyone affected with the disorder of stuttering.

Acknowledgements:

The research reported herein was completed in partial fulfillment of the requirements for the Ph.D. program at the University of Illinois at

Urbana-Champaign. Data were collected through the Illinois

International Stuttering Research Program (centered at the Department of Speech & Hearing Science) and were analyzed at the University of

Chicago, Department of Human Genetics in the School of Medicine. This research was supported by the United States’ National Institutes of

Health, National Institute on Deafness and Other Communication

Disorders, grants # R01 DC00459 (PI: Ehud Yairi), # R01 DC05210 (first

PI: Ehud Yairi; second PI: Nicoline Ambrose), and # RO1 DC04415 (PI:

Nancy Cox).

83

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