2024; 22(2): 295-305  https://doi.org/10.9758/cpn.23.1099
Epigenome-wide Association Study for Tic Disorders in Children: A Preliminary Study in Korean Population
Young Kyung Ko1,*, Suhyuk Chi2,*, Gyu-Hwi Nam3, Kyung-Wan Baek4, Kung Ahn5, Yongju Ahn5, June Kang6, Moon-Soo Lee2, Jeong-An Gim7
1Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea
2Department of Psychiatry, Korea University Guro Hospital, Seoul, Korea
3PhileKorea Technology Co. Ltd., Daejeon, Korea
4Department of Physical Education, Gyeongsang National University, Jinju, Korea
5HuNBiome Co. Ltd., Seoul, Korea
6Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
7Department of Medical Science, Soonchunhyang University, Asan, Korea
Correspondence to: Moon-Soo Lee
Department of Psychiatry, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea
E-mail: npboard@korea.ac.kr
ORCID: https://orcid.org/0000-0003-0729-6943

Jeong-An Gim
Department of Medical Science, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myeon, Asan 31538, Korea
E-mail: vitastar@sch.ac.kr
ORCID: https://orcid.org/0000-0001-7292-2520

*These authors contributed equally to this work.
Received: June 14, 2023; Revised: September 24, 2023; Accepted: November 24, 2023; Published online: January 10, 2024.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Objective: Tic disorders can affect the quality of life in both childhood and adolescence. Many factors are involved in the etiology of tic disorders, and the genetic and epigenetic factors of tic disorders are considered complex and heterogeneous.
Methods: In this study, the differentially methylated regions (DMRs) between normal controls (n = 24; aged 6−15; 7 females) and patients with tic disorders (n = 16; aged 6−15; 5 females) were analyzed. We performed an epigenome-wide association study of tic disorders in Korean children. The tics were assessed using Yale Global Tic Severity Scale. The DNA methylation data consisted of 726,945 cytosine phosphate guanine (CpG) sites, assessed using the Illumina Infinium MethylationEPIC (850k) BeadChip. The DNA methylation data of the 40 participants were retrieved, and DMRs between the four groups based on sex and tic disorder were identified. From 28 male and 16 female samples, 37 and 38 DMRs were identified, respectively. We analyzed the enriched terms and visualized the network, heatmap, and upset plot.
Results: In male, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed hypomethylated patterns in the ligand, receptor, and second signal transductors of the PI3K-Akt and MAPK signaling pathway (most cells were indicated as green color), and in female, the opposite patterns were revealed (most cells were indicated as red color). Five mental disorder-related enriched terms were identified in the network analysis.
Conclusion: Here, we provide insights into the epigenetic mechanisms of tic disorders. Abnormal DNA methylation patterns are associated with mental disorder-related symptoms.
Keywords: Tic disorder; DNA methylation; EWAS; YGTSS
INTRODUCTION

Tic disorder is a neurodevelopmental disorder characterized by involuntary, sudden, and repetitive movements and vocal sounds. Tics typically begin in childhood and can adversely affect a child’s social behavior, academic performance, and other developmental processes into adolescence. As of now, there are no curative treatments for tic disorders [1]. The disorder is influenced by a combination of genetic, epigenetic, and environmental factors [2]. However, due to the ambiguity caused by small sample sizes or inconsistent phenotype definitions in previous studies, no diagnostic markers— including genetic or epigenetic markers—related to the treatment and prognosis of tic disorders have been identified [3]. Furthermore, research into the genetics of tic disorders is limited because mental illnesses, including tic disorder, result from a complex interplay of various factors. Hence, definite evidence has not yet been obtained at the genetic level. In this study, we aim to identify the epigenetic patterns that are associated with tic disorder.

Epigenetic mechanisms regulate gene expression in response to external environmental conditions. At the molecular level, epigenetics include histone modifications and changes in DNA methylation, non-coding RNAs, and chromatin structure [4]. This research will primarily concentrate on DNA methylation. DNA methylation refers to the addition of a methyl group to cytosine phosphate guanine (CpG) sites in DNA. These methylation profiles can shift in response to external or internal conditions, thereby regulating gene expression. Additionally, these alterations are also exhibited in response to disease, making them valuable for assessing disease progression and understanding underlying mechanisms. Neurological disorders, in particular, frequently exhibit significant roles for DNA methylation in their pathophysiology [5,6].

Previous research has investigated the role of DNA methylation in the development of tic disorders. Sánchez Delgado et al. [7] found no methylation-associated alterations in the KCNK9 and TRAPPC9 genes—both located in chromosome 8—in Tourette syndrome patients when compared to controls. In the study, Zilhão et al. [8] conducted an epigenome-wide association study (EWAS) on tic disorders. They observed 411,169 autosomal methylated CpG sites and analyzed the phenotypes and DNA methylation data of 1,678 participants, encompassing the differentially methylated regions (DMRs) between 188 cases and a few-hundred-large subset of 1,490 controls. The probe cg15583738, located in an intergenic region on chromosome 8 (p = 1.98 × 10−6), was reported to have the strongest association. Several top-ranking probes were also found in or near the genes previously linked to neurological disorders [8]. In another EWAS, researchers analyzed genome-wide DNA methylation patterns in whole blood samples. The top two identified genes (TSC1 and CRYZ/TYW3) and enriched pathways and components (phosphoinosides and PTEN pathways and insulin receptor substrate binding) were associated with the PI3K/AKT/mechanistic target of rapamycin pathway [9]. The outcome of these studies revealed similar patterns between cases and controls. This underscores the need for further research into tic disorders for identifying methylation patterns through comparison with earlier studies.

Abnormal DNA methylation patterns have been observed in patients with mental disorders, and DMRs have been identified in samples from individuals with depression and suicidal ideation [10-13]. Likewise, genome-wide DNA methylation profiles have also been confirmed in mental disorders such as attention-deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder [14-17]. A more specific comparison of DNA methylation between groups can further elucidate the role of DNA methylation in the pathophysiology of tic disorders. Given that the phenotype of a mental disorder is influenced by numerous external factors, establishing a direct cause-and-effect relationship can be challenging. However, even in the absence of a direct link, our analysis can aid to investigate the involvement of DNA methylation in the pathophysiology of tic disorders, enhancing our current understanding of the disease. Our primary objective was to investigate the different DNA methylation patterns between patients and controls. Since differences in both clinical manifestations and DNA methylation patterns between male and female patients have been documented [18-21], we also divided our participants into four groups based on the presence of tic disorder and their sex.

METHODS

Participants

A total of 40 participants were enrolled in this study, including 24 healthy controls (7 females aged 7−15; 17 males aged 6−14) and 16 patients with tic disorders (5 females aged 6−13; 11 males aged 6−15). Patients were recruited from the Department of Psychiatry, Korea University Guro Hospital between July 2021 and December 2022. All participants were between the ages of 6−15 and had no past medical history of neurologic disorders, head trauma, tumors, or seizures that could affect psychiatric conditions. Tic disorder patients were diagnosed by trained child and adolescent psychiatrists based on the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders. All participants were assessed using the Korean version of the Kiddie-Schedule for Affective Dis-orders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) for possible psychiatric comorbidities [22]. Tics were assessed using the Yale Global Tic Severity Scale (YGTSS). The YGTSS is a clinician-rated instrument considered as the gold standard for assessing tics in patients with Tourette’s syndrome and other tic disorders [23]. Healthy controls were recruited from kindergartens and schools in Seoul City and Gyeonggi Province, Korea. The research processes and all experimental protocols were approved by the Institutional Review Board (IRB) of the Korea University Guro Hospital (2021GR0275). Written informed consent was obtained from the parents or legal guardians of each participant. All methods were carried out in accordance with the relevant IRB guidelines, regulations, and the Declaration of Helsinki.

Sample and Data Processing

Blood (10 ml) was collected from the participants in ethylenediaminetetraacetic acid (EDTA) tubes (BD 367525; Vacutainer Whole Blood K2 EDTA Hemogard Cls) and frozen at −70°C until the tests were conducted. Total DNA was isolated using a DNA extraction kit (QIAGEN 69504; DNeasy Blood & Tissue Kits), according to the instructions of the manufacturer.

One microgram of the extracted DNA was sent to Novogene Corporation (Singapore) for methylation microarray analysis. DNA quality was assessed using an Infinium FFPE QC kit (Illumina), and the DNA was restored using an Infinium HD FFPE DNA restore kit (Illumina). Bisulfite conversion was performed using the EZ-96 DNA methylation kit (Zymo Research), and a methylation microarray was performed using the Infinium MethylationEPIC BeadChip Kit (Illumina). The Illumina Infinium MethylationEPIC array enables the simultaneous measurement of the methylation marks at more than 860,000 CpG sites in almost all RefSeq genes. In this study, the EPIC array was used due to its ability to extract as many CpG sites as possible from blood, a sample that is relatively easily obtained. An iScan system (Illumina) was used to read the BeadChips.

Array data export processing and analysis were performed using the Illumina GenomeStudio version 2011.1 (Methylation Module version 1.9.0) and R version 4.1.2. Each data-point of methylation was represented by fluorescent signals from the methylated (M) and unmethylated (U) alleles. The ratio of fluorescent signals was computed from the two alleles as β = (max (M, 0)) / (|U| + |M| + 100). Raw β-values were determined for 865,918 CpG sites. Background correlations and dye bias equalization were performed using the lumi package in R. Beta-mixture quantile normalization was performed to reduce assay bias, using the BMIQ package in R.

Bioinformatic Analysis and Visualization

To analyze and visualize the characteristics between the two groups, we used R version 4.1.2. A total of 865,918 CpG sites were identified from the EPIC BeadChip analysis. Probes with missing or repeated values in all the samples were excluded, leaving a total of 726,945 CpG sites. The pvalue and fold change for these values were obtained using t-test between the two groups using “t.test” function. The genomic location for each CpG site was obtained (hg19) using the “Illumina-HumanMethylationEPICanno.ilm10b2.hg19” package of R. All source codes are available upon request from the corresponding author.

Differences between the groups were identified using a ttest and visualized using a volcano plot. All statistical analysis inputs were used as raw β-values. The thresholds of |fold change| and pvalue were defined, which were adjusted according to the DMR patterns between the two groups. A Manhattan plot was constructed using the “qqman” package in R. A heatmap and a hierarchical clustering plot were constructed using the “pheatmap” package in R. Volcano plots of DMRs were developed using the plot function in R with fold change on the x-axis and transformed −log10 (pvalues) on the y-axis. “pathfindR,” a tool package of R, was used to select the terms enriched in the identified DMRs, and the enriched terms were depicted using Upset plots via the “Upset_plot” function. To elucidate the key functions and pathways of DMRs, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, gene-concept network, and functional classification were analyzed. Statistical significance was set at p < 0.001. KEGG pathway enrichment analysis was done by clusterProfiler version 3.16.1 and the R package “pathview.” The gene-concept network, and functional classification were analyzed by the R package “DOSE”.

RESULTS

Demographic characteristics of both patient and control groups, as well as the YGTSS scores of the patient group (n = 16) were presented in Table 1. The control group was demographically similar to the patient group (n = 24). A total of 40 methylation data were presented by principal component analysis (Supplementary Fig. 1; available online).

Identification of DMRs between Patients with Tic Disorders and Healthy Controls

In 28 male participants, DMRs were identified using |fold change| > 0.1 and pvalue < 0.01 as thresholds. A total of 37 DMRs were identified: 7 DMRs were hypermethylated in tic disorder patients and 30 DMRs were hypermethylated in healthy controls. In 12 female participants, DMRs were identified using |fold change| > 0.12 and pvalue < 0.01 as thresholds. A total of 38 DMRs were identified: 28 DMRs were hypermethylated in tic disorder patients and 10 DMRs were hypermethylated in healthy controls. For the 16 tic disorder and 24 controls, DMRs were identified using |fold change| > 0.08 and pvalue < 0.01 as thresholds. A total of 36 DMRs were identified (data not shown). All DMRs were plotted as volcano plots in Figure 1A and 1B. DMRs were listed according to their β-values and were depicted as heatmaps in Figure 1C (males) and 1D (females). The pvalue of each genomic location was visualized using two Manhattan plots (Fig. 1E; male and Fig. 1F; female). We drew a Venn diagram for 38 males, 37 females, and 57 CpG sites of Zilhao’s EWAS [8] to determine common significant genes between our study and Zilhao’s results. However, there were no CpG sites corresponding to the intersection (Supplementary Fig. 2; available online).

Functional Enrichment Analysis of DMRs by CpG Sites

The “PathfindR” package was used to identify the enriched terms, which were visualized as heatmaps and Upset plots. For the 21 enriched terms, the average CpG methylation levels of each enriched term were indicated using a color scale, and hypermethylated enriched terms were colored red. In males, terms associated with fatty acid biosynthesis and cholesterol metabolism were detected and were found to be hypermethylated in the tic disorder group (Fig. 2A). Two terms related to signaling pathways (Chemokine, Insulin) were hypomethylated in the tic disorder group. An Upset plot was constructed using the heatmap of the enriched terms. The heatmap and the Upset plot contained 26 genes with 10 associated enriched terms. PTPN1, RUNX1, and SLC1A7 were hypermethylated in tic disorder patients, whereas the SLC1A1 genes, which are associated with glutamatergic synapse, were hypomethylated (Fig. 2B).

In females, 30 terms were detected and 28 terms were found to be hypermethylated in the tic disorder group (Fig. 3A). There was no relative difference in gap junction terms compared to other terms between the two groups. The lysine degradation term was hypomethylated in the tic disorder group. The heatmap and the Upset plot contained 12 genes with 10 associated enriched terms. HLA-DQB1, HLA-DPB1, and HLA-DPA1 were hypermethylated in tic disorder patients, which are associated with nine terms (Fig. 3B).

Network analysis was performed using “clusterProfiler.” In males, a total of five terms were retrieved and no term was directly related to psychiatric disorders. Each term was considered a node and the connected genes were linked as spokes (Fig. 2C). Five terms were clustered in the network and the most hypermethylated gene was PAWR. The gene was linked to transient ischemic attack terms. The network analysis of females showed that five terms related to psychiatric disorders were clustered, and most genes were simultaneously linked to five terms (Fig. 3C).

Further, we performed the KEGG pathway for 726,945 CpG sites, and the enriched KEGG terms were retrieved by the R package “clusterProfiler.” With pvalue < 0.001 as a cut-off value, KEGG terms were selected using the “enrichKEGG” function of the “clusterProfiler” package. The KEGG dot plot of the top 30 enrichment pathways is shown in Figure 2D and 3D. The PI3K-Akt signaling pathway and mitogen-activated protein kinase (MAPK) pathway was mapped at the top first and third term of male and female analysis, respectively (Figs. 2D, 3D). In the PI3K-Akt signaling pathway, all three ligands were hypomethylated in male (Fig. 4A), whereas hypermethylated in female Tic samples (Fig. 4B). Of a total of 13 membrane proteins, including eight transmembrane proteins, nine were hypomethylated in the male and nine were hypermethylated in the female. In the MAPK pathway, all five ligands (GF, TNF, IL1, FASL, TGFB) and four of the seven cell membrane receptors were hypomethylated in male (Fig. 4C), whereas four ligands of the five ligands and six receptors were hypermethylated in female tic disorder samples (Fig. 4D).

DISCUSSION

In this study, we obtained KEGG enrichment terms that may provide insights into the nature of tic disorder based on the DMRs derived from the EPIC array. We visualized two KEGG terms differentiated by sex and observed distinct patterns for both ligands and membrane receptors related with PI3K-Akt signaling and MAPK pathway.

Specifically, in males, the HLA-DRB1 gene was hypomethylated (Fig. 2B), whereas in females, three HLA family genes were hypermethylated (Fig. 3B). In addition, Akt, which is also known as protein kinase B and crucial for cell signaling, displayed differential methylation patterns on its gene between the sexes in patients with tic disorders. Enrichment terms related to immune responses and infections were chosen due to variations in the methylation levels of the ribosomal s6 kinase (RSK), a cell membrane receptor, and its ligand, the growth factors. The 90-kDa RSKs are a group of serine/threonine kinases that play important roles in the MAPK signaling cascade and are direct downstream effectors of ERK1/2 [24]. Ras/MAPK is a protein kinase specific to the amino acids serine and threonine. The MAPK pathway is an important regulator of diverse cellular processes. Evidence suggests that abnormalities in the MAPK pathway are related to psychiatric disorders, including Alzheimer’s disease [25,26]. Tourette syndrome is poorly understood but the MAPK signaling pathway may offer insights into its progression and treatment. The MAPK signaling pathway was ranked third in our KEGG analysis (Fig. 2D). MAPK signaling was altered in an animal model of Tourette syndrome [27]. In addition, an animal study showed that histidine decarboxylase knockout mice exhibited Tourette syndrome-like phenomenology [28]. This indirectly supports the involvement of MAPK signaling pathway in Tourrete syndrome since histidine decarboxylase produces histamine, which in turn may be involved in regulating the physiological responses of cells by interacting with the MAPK signaling pathway [29].

Factors related to mental health were also identified in our Gene Ontology (GO) term enrichment analysis. Interestingly, in the case of tic disorder, endocrine and pathway-related terms were hypermethylated, and melanoma- and glioma-related terms were hypomethylated. The GO terms “long-term depression”, “Notch signaling pathway”, and “prolactin signaling pathway” were hypermethy-lated in tic disorder patients. Moreover, JAK2, NCOR2, and PRKG1, which are key genes related to these pathways, were also hypermethylated in tic disorder patients. The terms presented in this study are related to ADHD and Tourette syndrome, and the relationship between dopamine receptors and synaptic plasticity has been explored in prior research [30].

This study holds its significance as it investigates DNA methylation in children and adolescents with tic disorders and normal controls within Korea, a nation marked by a predominantly homogeneous ethnicity. While our results did not consistently align with previous studies, they hint towards Korean-specific patterns and gender-specific differences. We utilized whole blood samples that can be obtained relatively easily, making our results valuable for upcoming tic disorder studies. However, a notable limitation of our study was a relatively small sample size. The current findings should be viewed as preliminary and will require validation through comparable cohorts. There is also a need to compare our results to those of Western populations in future studies. In addition, comparative analysis with other features (e.g., functional magnetic resonance imaging, electroencephalography, etc) that might contribute to the elucidation of the pathophysiology of tic disorders should be performed. While the study has the advantage of being free of confounding factors such as alcohol or smoking since it was conducted on children, there is a limitation in that it cannot control diet, exercise, or early life trauma that may affect DNA methylation.

In conclusion, we present further insights into the DNA methylation patterns of tic disorder patients. Our results suggest that methylated patterns that possibly relate to the essential pathophysiology of tic disorder might differ between males and females. Further in-depth studies will build on our current understandings on the disease.

Acknowledgments

The results of this study were partly presented at 34th CINP World Congress of Neuropsychopharmacology, 7−10 May 2023, Montreal, Canada.

Funding

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI21C0012).

Conflicts of Interest

Moon-Soo Lee, an editor of the Clinical Psychopharmacology and Neuroscience, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Author Contributions

Study concept and design: June Kang, Moon-Soo Lee, Jeong-An Gim. Acquisition, analysis, or interpretation of data: Young Kyung Ko, Suhyuk Chi, June Kang, Moon-Soo Lee, Jeong-An Gim. Project administration: Young Kyung Ko, Gyu-Hwi Nam, Jeong-An Gim. Methodology: Kyung-Wan Baek, Kung Ahn, Yongju Ahn, Jeong-An Gim. Drafting of the manuscript: Young Kyung Ko, Suhyuk Chi, Moon-Soo Lee, Jeong-An Gim. Critical revision of the manuscript for important intellectual content: Gyu-Hwi Nam, Kyung-Wan Baek, Kung Ahn, Yongju Ahn. Administrative, technical, or material support: Young Kyung Ko, Gyu-Hwi Nam, Yongju Ahn. Supervision: June Kang, Moon-Soo Lee, Jeong-An Gim.

Figures
Fig. 1. Visualization of differentially methylated regions (DMRs) between the samples from patients with tic disorders and healthy controls in two sexes. (A, B) Volcano plot illustrating DMRs (male: defined as a fold change in methylation > 0.1 (red) or < −0.1 (green) with pvalue < 0.01 (−log10 [pvalue] > 2); female: defined as a fold change in methylation > 0.12 (red) or < −0.12 (green) with pvalue < 0.01 (−log10 [pvalue] > 2) obtained through comparative analysis. In male, 37 DMRs were selected as per the cut-off and a heatmap was constructed. Seven (red dots) and 19 (green dots) DMRs were hypermethylated in tic disorder patients and healthy controls, respectively. In female, 38 DMRs were selected, and 28 (red dots) and 10 (green dots) DMRs were hypermethylated in tic disorder patients and healthy controls, respectively. Each point in the vol-cano plot represents the cytosine phosphate guanine (CpG) site that was analyzed in this study, and the red or green points that satisfied the threshold were visualized as a heatmap. (C, D) Heatmaps indicate the methylation levels of 37 and 38 DMRs in male and female, respec-tively. The three column annotation bars indicate age, sex, and tic disorder. The two row annotation bars indicate fold change and p values. In the fold change (FC) bar, the red color indicates the hyper-methylated CpG sites in tic disorder patients. The darker pvalue (PV) bar indicates statistical significance between the two groups. (E, F) Com-parative analysis with pvalues visualized as two Manhattan plots in male and female. The plots show −log10 pvalues for each SNP against the chromosomal location.
Fig. 2. Visualization of PathfindR analysis, gene-concept network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from differentially methylated regions (DMRs) between the two groups in male. (A) Heatmap of the enriched PathfindR terms. A total of 21 terms were retrieved, and the average cytosine phosphate guanine (CpG) methylation levels of each term is listed by a color scale. (B) Upset plot indicating the methylation patterns of 26 genes with 10 associated terms. (C) Network of the enriched terms and genes. The five nodes are the enriched terms, and the linked genes are connected as spokes. (D) The top 20 enriched KEGG pathways for DMRs.
Fig. 3. Visualization of PathfindR analysis, gene-concept network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from differentially methylated regions (DMRs) between the two groups in female. (A) Heatmap of the enriched PathfindR terms. A total of 30 terms were retrieved, and the average cytosine phosphate guanine (CpG) methylation levels of each term is listed by a color scale. (B) Upset plot indicating the methylation patterns of 12 genes with 10 associated terms. (C) Network of the enriched terms and genes. The five nodes are the enriched terms, and the linked genes are connected as spokes. (D) The top 20 enriched KEGG pathways for DMRs.
Fig. 4. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the PI3K-AKT (A, B) and mitogen-activated protein kinase (MAPK) (C, D) signaling pathway was mapped by fold changes. The red and green colors indicate hypermethylation in tic disorder patients and healthy controls, respectively. Left and right each signaling pathway was mapped in male (A, C) and female (B, D) analysis, respectively.
Tables

Demographics and clinical variables

Variable Tic (n = 16) Controls (n = 24) pvalue
Sex (male/female) 11/5 17/7
Age (yr) 9.13 ± 2.75 9.75 ± 2.44 0.468
IQ 93.44 ± 14.73 102.96 ± 11.28 0.037*
YGTSS score 24.57 ± 13.32
Motor tic score 6.19 ± 3.73
Phonic tic score 3.81 ± 4.40
Total tic score 20.00 ± 9.90
Impairment score 10.00 ± 5.16
Comorbidities (male/female)
ADHD 2/1

Values are presented as number only or mean ± standard deviation.

IQ, intelligence quotient; YGTSS, Yale Global Tic Severity Scale; ADHD, attention-deficit hyperactivity disorder.

*p < 0.05.

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