Whole exome sequencing identifies genes associated with Tourette’s Disorder in multiplex families

Abstract

Tourette’s Disorder (TD) is a neurodevelopmental disorder that affects about 0.7% of the population and is one of the most heritable neurodevelopmental disorders. Nevertheless, because of its polygenic nature and genetic heterogeneity, the genetic etiology of TD is not well understood. In this study, we combined the segregation information in 13 TD multiplex families with high-throughput sequencing and genotyping to identify genes associated with TD. Using whole-exome sequencing and genotyping array data, we identified both small and large genetic variants within the individuals. We then combined multiple types of evidence to prioritize candidate genes for TD, including variant segregation pattern, variant function prediction, candidate gene expression, protein-protein interaction network, candidate genes from previous studies, etc. From the 13 families, 71 strong candidate genes were identified, including both known genes for neurodevelopmental disorders and novel genes, such as HTRA3, CDHR1, and ZDHHC17. The candidate genes are enriched in several gene ontology categories, such as dynein complex and synaptic membrane. Candidate genes and pathways identified in this study provide biological insight into TD etiology and potential targets for future studies.

Introduction

Tourette’s Disorder (TD) is a neurodevelopmental disorder (NDD) that affects up to 1% of the global population ^{1, 2}. TD is characterized by chronic motor and vocal tics and is often diagnosed in early childhood. Some patients do not present the full spectrum of syndrome for TD and are characterized as TD-related chronic tic disorders, such as chronic motor tic disorder or chronic vocal tic disorder ^{1, 2}. TD has a high comorbidity with several psychiatric disorders: about 60% of patients are also diagnosed with attention-deficit hyperactivity disorder (ADHD) ^3–6 and 40%−60% with obsessive-compulsive disorder (OCD) ^7–10. Autism spectrum disorder (ASD) is also overrepresented in TD, and around 20–40% of individuals with ASD experience tics ¹¹. It is estimated that the lifetime prevalence rate of comorbid psychiatric disorders for TD patients is up to 90% ⁹.

TD is a highly heritable polygenic neuropsychiatric disorder (population-based-h² = 0.77; SNP-h² = 0.58) ^{12, 13}. The empirical recurrence risk estimates for TD and other chronic tic disorders in first-degree relatives is about 30% ¹⁴. Because of the high heritability of TD, many studies have been conducted to identify the genetic etiology of TD (reviewed in ¹⁵). Early TD genetic studies focused on mutations in single genes under the assumption of a monogenic inheritance model. Although some of the candidate genes were reported, mutations in these genes only explain a few of the TD cases (reviewed in ¹⁵). TD is now believed to have a complex multigenic allelic architecture, similar to other NDDs ¹⁶. More recently, several large studies were performed to identify genes associated with TD, including whole-exome sequencing (WES) studies of simplex families focusing on de novo mutations ^{17, 18} and a meta-analysis of Genome-Wide Association Studies (GWAS) with about 5,000 TD patients ¹³. However, only a few susceptibility genes/loci were identified in the WES and GWAS studies.

Due to its high heritability, one effective approach to identify TD candidate genes is to study large multiplex families. Children of TD patients are between 10 and 100 times more likely to be affected ^{14, 19}, indicating shared genetic susceptibility among family members. Therefore, variant segregation pattern in multiplex families can help identify inherited rare variants with strong effects and provide additional information compared to simplex trios. The Tourette International Collaborative Genetics Study (TIC Genetics) is a collaboration that recruits TD affected families for studying the genetic factors of TD ²⁰. In the current study, we generated WES and genotyping array data for individuals from 13 TIC Genetics multiplex families. We then prioritized risk genes with rare mutations in these families to identify potential risk genes for TD.

Subjects and Methods

Human subjects

Multiplex families were recruited through the TIC Genetics study ²⁰ and the New Jersey Center for Tourette Syndrome (NJCTS) ²¹ with informed consent from all participants. The study protocol was locally approved at all TIC Genetics sites. All sequenced individuals were categorized as “white” race, except for the sperm donor (4001) in FAM4, whose ancestry is unknown (Table 1). The clinical assessment and definition of TD and chronic tic disorders were described in detail previously ²⁰, and were based on the Diagnostic and Statistical Manual of Mental Disorders – Fourth edition, Text Revision (DSM-IV-TR) or Fifth edition (DSM-5) ^{22, 23}. In this study, a TD-affected individual is defined as an individual who was diagnosed with TD or other tic disorders (i.e., chronic motor or vocal tic disorder, a combined subtype, transient tic disorder or tic disorder-Not Otherwise Specified ²⁰). A multiplex family is defined as a family with at least three TD-affected individuals.

Table 1.

Summary of the multiplex families

Family	Total Ind	TD	TD with OCD	TD with ADHD	WES (TD)	SNV/Indel	SNV/Indel Filtered	pVAAST (d)	pVAAST (r)	Candidate	Genotyping (TD)	CNV	CNV exonic
FAM1	9	8	5	2	6 (5)	103,621	14,911	35	5	34	6 (5)	276	134
FAM2	9	7	4	5	6 (6)	101,590	14,917	199	8	81	7 (6)	170	56
FAM3	15	7	1	0	12 (7)	136,028	27,180	89	10	26	12 (6)	261	85
FAM4	18	11	5	2	17 (11)	167,676	43,915	10	25	33	0	NA	NA
FAM5	12	8	4	1	10 (7)	140,006	28,164	1	NA	1	11 (7)	240	44
FAM6	9	5	1	0	6 (4)	124,489	22,345	50	NA	40	6 (4)	142	30
FAM7	8	4	0	0	8 (4)	124,813	22,209	35	NA	32	8 (4)	184	37
FAM8	5	3	3	1	4 (3)	114,025	17,741	136	NA	125	4 (3)	121	62
FAM9	4	3	1	3	4 (3)	105,502	15,191	93	11	91	4 (3)	116	45
FAM10	5	3	2	1	5 (3)	110,102	17,159	51	10	50	0	NA	NA
FAM11	18	9	3	2	13 (8)	133,278	27,134	18	NA	16	14 (9)	289	57
FAM12	16	8	2	0	11 (6)	129,089	23,991	14	NA	13	0	NA	NA
FAM13	23	6	0	0	8 (5)	123,730	21,873	218	NA	50	0	NA	NA
Total	151	82	31	17	110 (72)	280,363	139,092	949	69	543	72 (47)	1,799	550

Total Ind: individuals surveyed with phenotypes in each family.

TD: individuals diagnosed as TD (Tourette Disorder, Chronic Tic Disorder-Motor subtype, Chronic Tic Disorder-Vocal subtype, Chronic Tic Disorder-Combined subtype, transient tic disorder or tic disorder-Not Otherwise Specified. see Methods for diagnosis detail).

TD with OCD: individuals with both TD and OCD diagnosis.

TD with ADHD: individuals with both TD and ADHD diagnosis.

WES (TD): individuals subjected to whole exome sequencing in each family, the number of individuals diagnosed with TD are shown in parenthesis.

SNV/Indel: SNVs (single nucleotide variants) or Indels (insertion and deletions) in each family in the variant call results.

SNV/Indel Filtered: variants after removing those with AF ≥ 10% in the 1000 Genomes project or ≥ 5% in ExAC.

pVAAST genes (d)/ pVAAST genes (r): candidate gene counts reported by pVAAST running in the dominant/recessive (d/r) mode.

Candidate: candidate gene counts after filtering.

Genotyping (TD): the number of individuals subjected to microarray analysis for CNV detection and the number of individuals diagnosed with TD are shown in parenthesis.

CNV: count of candidate CNVs in the copy number variants analysis.

CNV exonic: count of candidate CNVs with overlap with exon regions of gene models.

Whole-exome sequencing, variant calling and annotation

SureSelect Human All Exon V4, Human All Exon V4+UTR (Agilent Technologies, Santa Clara, CA, USA), or NimbleGen SeqCap EZ Exome V2 (Roche, Wilmington, MA, USA) kits were used for the WES library preparation. Sequencing was performed on the Illumina HiSeq platform with 100 PE or 150 PE format (Illumina, San Diego, CA, USA). Variant calling was performed using the Genome Analysis Toolkit (GATK) following the best practice pipeline ²⁴ and variant annotation was performed using ANNOVAR ²⁵. To control for the batch effect, joint variant calling was performed on all samples and only variants that are in the smaller set of the enrichment regions (SeqCap EZ Exome V2) among the multiple exome capture kits were considered (see Supplemental Methods for detailed steps and commands). The sequencing data is available at dbGaP under study accession phs001423.v2.p2.

Candidate gene prioritization, annotation, and filtering

pVAAST (pedigree Variant Annotation, Analysis and Search Tool) was used to identify candidate genes in each pedigree ^{26, 27}. pVAAST is a likelihood-based tool that prioritize candidate genes using several types of variant information in each gene, including the segregation pattern, the predicted functional impact, and the allele frequency (AF) in general populations. pVAAST was run under dominant mode of inheritance for all families and under recessive mode of inheritance for families where the recessive mode of inheritance cannot be ruled out (Table 1, see Supplemental Methods for details).

For gene function predictions, the pLI (probability of being loss-of-function intolerant) score and the missense Z score were extracted from gnomAD for each gene ²⁸. Brain developmental gene expression data were obtained from the Gene Tissue Expression project (GTEx) ^{29, 30}, the BrainSpan Atlas of the Developing Human Brain project ³¹, and the Human Developmental Biology Resource (HDBR) ³² (see Supplementary Methods for detail). Diseases related to NDDs were extracted and curated from the DISEASES database ³³ (https://diseases.jensenlab.org). The gene knock-out mouse behavior were downloaded from the International Mouse Phenotyping Consortium (IMPC) ³⁴ (ftp://ftp.ebi.ac.uk/pub/databases/impc/latest/).

Gene lists from previous NDD studies

Risk genes for several NDDs were collected from previous studies, including TD, OCD, ADHD, ASD, intellectual disability, epileptic encephalopathies, and schizophrenia. The gene sets and the genes in the gene sets are summarized in Table S1.

Protein–Protein interaction network, Gene Ontology, pathway, and protein complex enrichment analysis

Three databases were used to investigate Protein-Protein Interaction (PPI) networks among candidate genes, including STRING ³⁵, ConsensusPathDB ³⁶, and GIANT_v2 ^{37, 38} (see Supplementary Methods for detail). These databases were shown to have the best performance in a recent benchmark paper ³⁹. The benchmark paper also showed that interactions present in at least two databases provide higher confidence than those specific to one database.

Enrichment analyses were performed for all NDD genes with over-representation analysis provided by ConsensusPathDB ³⁶. An enriched term (i.e., Gene Ontology (GO), pathway, or protein complex) was selected for further analysis if: 1) the total number of genes belong to the term is ≤ 200; 2) the term includes more than one gene from TD_multiplex gene list and more than two genes from all TD genes (TD_multiplex + TD_simplex + TD_CNV); and 3) the term includes genes from more than one multiplex family. Enrichment p-value for each gene list was calculated with Fisher’s exact test (see Supplementary Methods for detail).

Copy Number Variant (CNV) analysis

Genotyping was performed at the Keck Biotechnology Resource Laboratory at Yale University School of Medicine and at RUCDR Infinite Biologics® at Rutgers University using the Illumina HumanOmni1-Quad or OmniExpressExome BeadChip (Illumina, San Diego, CA). Samples from each pedigree were genotyped on the same platform. Genotype calling and CNV detection were performed as previously described ⁴⁰ (see Supplementary Methods for detail). A pCNV threshold of <=0.05 was used for initial selection of CNVs. CNV annotation was performed by CNVision, AnnotSV ⁴¹, and a custom program for inheritance pattern analysis. CNVs that are smaller than 1,000 base pairs or larger than 2 million base pairs were excluded.

Results

Families and phenotypes

Figure 1 describes the overall design of the project. A total of 13 multiplex families were included in this study. Family size ranged from 4 to 23, with a total of 151 individuals (Table 1, Figure S1). The percentage of individuals diagnosed with TD and other chronic tic disorders was 60% (51/85) and 47% (31/66) for males and females, respectively. Details of individual phenotypes are summarized in Table S2.

Figure 1. Overall study design.

WES: whole-exome sequencing; SNP: single nucleotide polymorphism; AF: allele frequency; CNV: copy number variant; GO: gene ontology; PPI: protein-protein interaction.

Whole-exome sequencing and candidate gene prioritization

Among the 13 families, we obtained DNA samples from 110 individuals for WES, including 72 individuals with TD and 38 non-TD family members. At least three individuals with TD were sequenced in each family (Table 1). Overall, the 110 individuals sequenced had an average mean depth of coverage of 42.5x and an average median coverage of 34.9x (Table S2). Using the sequencing data, we identified single nucleotide variants (SNVs) and small insertions and deletions (indels). The total number of variants that passed quality control was (6.64 ± 0.72) × 10⁴ in each individual. After removing common variants (see Methods and Supplemental Methods), the number of variants in each individual ranged from 3,970 to 8,673, with an average of 6,710 (Table S2).

Next, we used the candidate gene prioritization tool pVAAST to identify candidate genes in each pedigree. The number of candidate genes reported among the 13 families varied from 1 to 218, with a total of 1,018 genes (Table 1). Because the variant segregation pattern is a major factor for candidate gene prioritization in multiplex families, small families (e.g., FAM9) and families where all individuals were affected (e.g., FAM2) have less power to distinguish variant segregation patterns, thus producing a higher number of candidate genes than the larger families. To reduce the number of candidate genes, we applied additional filters based on variant segregation pattern (true positive events ≥ 2 and false rate < 0.3), gene expression level in brain (max TPM > 5), and AF in general populations (gnomAD 2.1.1 AF < 0.05) (see Methods and Supplemental Methods for detail). After filtering, the number of candidate genes ranged from 1 to 125 among the 13 families, with a total of 543 unique genes (Table 1). Hereafter we refer to these genes as “TD_multiplex” genes (Table S3). Among the 543 unique genes, 25 were identified in more than one family, most of which were identified in families with many candidate genes (Table S2).

Fourteen of the TD_multiplex genes were identified in a previous study of TD simplex trio families ¹⁸ (Table S4). In addition, two genes were reported in previous TD studies: DNAJC13 (DnaJ heat shock protein family (Hsp40) member C13) ⁴² and SLC6A4 (solute carrier family 6 member 4) ⁴³. Some of these genes are also reported to be associated with other NDDs (Table 2, Table S1B). For example, ANK3 (ankyrin 3) has been reported to be associated with ADHD and ASD, in addition to TD.

Table 2.

Top candidate genes in each family

			Variant Annotation						Gene Annotation
Family	Gene	Confidence	Chr	Position	AF	mutation	Effect	Segregation	pLI	mis_z	Other NDDs	TPM1	TPM2	TPM3	IMPC	Interaction
FAM1	CASP8	H	2	202,134,324	1.33E-05	I->V	DT	0|6	0.00	1.01		3	4	7		59
FAM1	GAK	H	4	862,363	7.30E-03	D->Y	DD	0|6	0.01	0.02		145	44	22		103
FAM1	PNKD	H	2	219,204,814	3.33E-05	Q->stop	..	0|6	0.00	0.32		167	254	52		4
FAM1	ABCA7	M	19	1,048,950	6.40E-05	G->R	DD	0|5	0.00	−1.47	ASD	66	45	19	F	5
FAM2	CX3CL1	H	16	57,413,660	5.10E-03	A->V	TD	1|6	0.61	0.84	TD_simplex	124	1165	43	T	36
FAM2	DNAH7	H	2	196,866,433	1.07E-02	M->R	DD	1|6	0.00	−1.09		6	6	30		5
FAM2	SYT17	H	16	19,194,929	4.00E-04	E->D	DD	1|6	0.00	1.18	ASD	47	150	24		3
FAM2	AKAP9	M	7	91,631,542	9.57E-05	A->T	TT	1|6	0.00	−0.13	ASD	42	99	86		88
FAM2	NIPA1	M	15	23,048,912	1.10E-05	V->M	..	1|6	0.01	1.94	TD_CNV, ASD	149	293	49	F	4
FAM2	SLC6A2	L	16	55,725,894	9.55E-05	T->M	TD	1|6	0.08	2.02	ADHD	0	1	9	F	27
FAM3	CDH23	H	10	73,270,925	3.00E-04	A->T	TD	2|12	0.00	0.71	ADHD	42	97	38	T	10
FAM3	CDHR1	H	10	85,972,932	4.50E-03	N->S	DT	1|12	0.00	−0.70		79	150	45		1
FAM3	ESR1	H	6	152,265,352	5.00E-04	R->C	DD	2|12	1.00	1.61		3	9	4		163
FAM3	OPRM1	H	6	154,411,110	3.60E-03	S->C	DD	2|12	0.00	−0.62	ADHD	3	13	7		51
FAM3	ABCC1	L	16	16,138,519	8.88E-05	S->F	DT	2|12	0.00	1.77		19	36	37		12
FAM3	MAPK3	L	16	30,128,265	1.90E-03	E->K	DD	3|12	0.04	1.74	ASD	404	531	64		180
FAM4	CENPJ	H	13	25,458,540	3.18E-05	E->stop	DD	2|12	0.00	0.22		42	41	105	T	48
FAM4	ZDHHC17	H	12	77,243,235	3.20E-05	I->T	TD	1|12	1.00	2.57		91	134	165		31
FAM4	ADM	M	11	10,327,330	.	S->W	DD	1|12	0.04	−0.11		58	332	117		38
FAM4	DCAF1	M	3	51,475,633	8.03E-06	K->R	.T	0|12	1.00	4.84		36	87	47		46
FAM5	TADA3	H	3	9,831,246	1.00E-03	R->C	DT	1|10	0.00	2.12		142	218	72		20
FAM6	KCNH5	H	14	63,246,495	1.00E-04	A->G	DD	0|5	0.02	2.51		9	83	11		20
FAM6	RAB11FIP3	H	16	569,754	3.00E-04	E->Q	TD	0|5	1.00	1.27	TD_simplex	182	158	86	T	12
FAM6	RIMS1	H	6	73,017,069	2.00E-04	S->Y	DD	1|6	0.99	2.01	ASD	169	282	34		19
FAM6	AEBP1	L	7	44,144,407	3.19E-05	E->G	DT	1|6	0.00	1.00		86	182	106	F	22
FAM7	CADPS2	H	7	122,033,277	2.00E-04	S->L	DT	1|8	0.37	1.09	ASD	332	679	83	F	4
FAM7	NINL	H	20	25,457,679	6.00E-04	A->S	DD	0|8	0.00	−0.98	ASD	63	61	68	F	40
FAM7	POFUT1	H	20	30,816,172	3.18E-05	E->K	TT	0|8	0.96	0.95		18	46	35		4
FAM7	AHCY	L	20	32,880,242	3.60E-03	G->R	DD	0|8	0.04	1.55		109	361	263	F	51
FAM8	ANK3	H	10	61,833,414	3.40E-03	S->S	DD	0|4	1.00	2.79	TD_simplex, ASD, ADHD	107	218	96		24
FAM8	DNAH5	H	5	13,807,706	9.56E-05	M->I	TT	0|4	0.00	−0.77	TD_simplex	1	6	20	F	8
FAM8	GABRB3	H	15	27,018,841	3.20E-03	P->S	TT	0|4	0.95	3.39	TD_CNV, ASD, OtherNeuro	58	182	81		15
FAM8	GPLD1	H	6	24,429,341	4.69E-03	Q deletion	..	0|4	0.00	−0.30	TD_simplex	36	42	14	F	17
FAM8	IGF2BP1	H	17	47,117,430	6.70E-03	H->Q	TT	0|4	1.00	3.52	TD_simplex	0	113	290		27
FAM8	NLGN3	H	X	70,389,354	1.90E-03	T->A	TT	0|4	0.98	4.21	ASD	90	315	109	T	21
FAM8	KIDINS220	M	2	8,928,870	.	A->V	DD	0|4	0.07	2.26		99	792	376		20
FAM8	NDUFB1	M	14	92,583,927	4.30E-03	M->L	TT	0|4	0.00	−0.21		249	667	218		12
FAM8	VPS13A	M	9	79,938,036	3.80E-03	R->C	DT	0|4	0.00	1.76		22	33	26	F	26
FAM8	CAPN6	L	X	110,495,596	9.12E-05	K->M	DD	0|4	0.82	1.52		0	13	151		1
FAM8	SUGP1	L	19	19,414,217	.	R->G	DD	0|4	1.00	1.21		76	132	39	F	26
FAM9	AGRN	H	1	979,748	3.21E-02	E->V	DD	1|4	0.00	0.23		127	185	149		53
FAM9	DNAJC13	H	3	132,221,143	2.20E-03	R->H	DD	0|4	1.00	2.28		28	47	50		16
FAM9	UNC13C	H	15	54,306,012	3.20E-03	D->E	DD	0|4	0.00	−0.14		77	327	16	F	1
FAM9	VGF	H	7	100,806,657	.	P->A	DD	0|3	0.57	0.25		455	801	83	T	21
FAM9	SORT1	M	1	109,910,100	2.20E-03	I->V	TT	0|4	1.00	2.10		440	544	102	F	28
FAM9	SYNJ2	M	6	158,492,660	3.40E-03	T->M	DD	0|4	0.00	0.72		230	125	47	F	34
FAM9	ALDH7A1	L	5	125,880,710	4.50E-03	T->A	DD	0|4	0.00	0.37		96	138	139		9
FAM9	DHDDS	L	1	26,764,735	2.50E-03	R->Q	TT	0|3	0.25	1.09		185	145	25	F	1
FAM10	MAP1LC3B	H	16	87,432,453	7.10E-03	E->Q	TD	1|5	0.00	0.29		291	511	180		42
FAM10	SLC6A4	H	17	28,538,374	6.00E-04	I->V	TT	1|5	0.25	1.93	ASD, ADHD	1	8	19		25
FAM10	GPR37	M	7	124,404,617	2.00E-04	N->K	TT	1|5	0.00	0.84	ASD	377	541	56		44
FAM10	ROBO3	M	11	124,740,144	3.80E-03	D->N	DD	1|5	0.00	0.61		26	38	688		2
FAM10	VWC2	M	7	49,951,764	4.00E-04	E->Q	DD	1|5	0.97	1.67		43	137	19		1
FAM11	CNTNAP5	M	2	125,504,806	4.00E-04	D->G	TT	2|13	0.99	0.39	ASD	3	94	35	F	0
FAM11	HTRA3	M	4	8,293,193	7.40E-03	V->M	DD	1|13	0.00	0.50		3	44	43	F	14
FAM11	NCKAP5L	M	12	50,189,151	.	G->V	TT	1|13	0.63	0.74		49	40	33		3
FAM11	DNAH11	L	7	21,737,777	.	S->R	DD	3|13	0.00	−5.61		1	15	33		4
FAM11	NRCAM	L	7	107,836,262	3.00E-04	N->S	TT	3|13	0.02	1.91	ASD	243	675	309	T	21
FAM12	TNN	H	1	175,097,769	2.50E-03	V->I	TT	0|11	0.00	−0.54		0	11	13		8
FAM12	COL4A2	M	13	111,082,914	4.00E-04	H->L	TT	1|11	0.00	2.19		19	925	439	F	62
FAM12	NCL	M	2	232,325,406	6.09E-03	DE deletion	..	0|11	1.00	0.85		386	660	1028		95
FAM12	DYNC2H1	L	11	103,339,392	1.16E-05	V->M	TD	0|11	0.00	0.91		9	19	70	F	13
FAM12	NAALAD2	L	11	89,896,570	6.00E-04	R->W	DD	0|11	0.00	0.47		22	24	53		3
FAM12	STARD9	L	15	42,983,572	1.10E-03	G->S	TT	0|11	0.00	2.50		36	26	30	F	1
FAM13	DNAH3	H	16	21,098,323	5.70E-03	R->S	DD	1|8	0.00	0.03	ASD	1	5	22		9
FAM13	ETS1	H	11	128,426,219	.	E->K	DD	1|8	0.78	2.52	TD_simplex	16	70	219		32
FAM13	GRIN2A	H	16	9,858,173	3.70E-03	N->K	TD	1|8	1.00	2.83	ASD, ADHD, OtherNeuro	45	281	17	F	38
FAM13	TUBGCP5	H	15	22,846,897	7.50E-03	V->I	..	1|8			TD_CNV, ASD	23		32		24
FAM13	EPHB2	M	1	23,233,346	2.90E-03	D->N	DT	1|8	1.00	2.45	ASD	11	200	161		50
FAM13	KCNK1	M	1	233,802,497	7.10E-03	R->R	TD	1|8	0.03	0.72		261	501	154		22
FAM13	PCDH12	L	5	141,336,635	3.30E-03	T->M	DD	1|8	0.00	0.78		13	24	20	F	1

Confidence: confidence of genes as causal for TD based on evidence collected. H, high; M, medium; L: low.

AF: allele frequency in gnomAD. If a variant does not exist in gnomAD, the ExAC AF is used. Variants not found in both databases are empty.

Effect: deleterious effect predicted by SIFT and Pholyphen-2. D for deleterious, T for tolerated, “.” for data not available.

Segregation: the inconsistency between the observation and the expected mutation segregation in individuals. The text is: (false positive + false negative) | (total - unknown). Genes with empty values are candidate genes from pVAAST run using recessive mode.

pLI: probability of being loss-of-function intolerant score. Typically, pLI ≥ 0.9 is a cutoff for extremely loss of function intolerant.

mis_z: Z score for the deviation of observed counts from the expected number of missense mutations. Positive Z scores indicate increased intolerance to variation.

Other NDDs: overlap with other neurodevelopmental disorder gene lists in Table S1.

TPM1, TPM2, TPM3: max TPM values of brain tissues in GTEx, BrainSpan, and HDBR, respectively.

IMPC: mouse knock-out model results from IMPC. T: genes with “behavior/neurological phenotype” or “nervous system phenotype”; F: genes without “behavior/neurological phenotype” or “nervous system phenotype”; Empty: data not available.

Interaction: Number of interactions with NDD_all genes (excluding risk genes from the same family) defined by STRING, GIANT_v2 or ConsensusPathDB.

Candidate genes in individual families

To understand the functional impact of TD_multiplex candidate genes, we collected several types of evidence for each gene, including their expression pattern in brain, known disease association, tolerance to mutations, knock-out mouse phenotypes, and their interaction with other genes (Figure 2, see Methods and Supplemental Methods for detail). We favored candidate genes that contain variants with good segregation patterns, low AF in the generation population, and genes that were identified in previous studies of TD or other NDDs, with nervous system related functions, with higher or tissue-specific expression in brain, and genes that causes a related-behavior change in mouse knockout models. Using these annotations, we further refined the TD_multiplex candidate genes and selected a final list of 71 candidate genes (Table 2). Below we briefly describe the high-confidence top candidate genes in several families.

Figure 2. Information used for variant and gene prioritization.

Detailed description of the “Annotation” fields can be found in the Supplemental Methods and Table 2. PPI: Protein-protein interaction; GO: Gene Ontology; pLI: probability of being loss-of-function intolerant score; mis_z: Z score for the deviation of observed counts from the expected number of missense mutations; TPM: Transcript Per Million.

FAM3

CDHR1 (Cadherin Related Family Member 1) was the only candidate gene with a perfectly segregating variant within the pedigree (ENST00000372117.3:p.Asn623Ser). The mutation is in the Cadherin domain which was predicted to be deleterious by SIFT ⁴⁴. CDHR1 shows specific high expression in brain regions including caudate, hypothalamus, and nucleus accumbens ^{29, 30}. CDHR1 is a calcium-dependent cell-cell adhesion membrane protein and is involved in vision related diseases such as cone-rod dystrophy ⁴⁵.

FAM5

TADA3 (Transcriptional Adaptor 3) was the top candidate gene as ranked by pVAAST. The candidate mutation is a nonsynonymous mutation (ENST00000301964.2:pArg171Cys) that is predicted to be deleterious by SIFT and damaging by Polyphen-2 ⁴⁶. The variant was present in all seven affected individuals in the three-generation pedigree, but also present as a heterozygous variant in the unaffected grandfather. This gene is a component of the histone acetyl transferase (HAT) coactivator complex and a regulator of p53 and it is important for chromatin modulation and cell cycle progression ⁴⁷. TADA3 is universally highly expressed in brain regions (> 70 TPM in the GTEx data) ³⁰. As a multifaceted protein, TADA3 plays an important role in chromatin remodeling, cellular proliferation, cellular senescence, DNA damage response, and embryonic development ⁴⁷.

FAM11

HTRA3 (HtrA Serine Peptidase 3) had a nonsynonymous mutation (ENST00000307358.2:p.Val269Met) in the protease domain, predicted to be deleterious by SIFT and Polyphen-2. The mutation was present in all eight affected individuals and an unaffected grandchild. HTRA3 is highly expressed (TPM > 10) during some of the early brain developmental stages ^{31, 32}. HTRA3 is a secreted protein with four domains, which is highly conserved in vertebrates ⁴⁸. Several mouse models for Wolf–Hirschhorn syndrome with deletions of Htra3 cause significant phenotypic changes including seizures, a behavior similar to tics ⁴⁹.

FAM12

NCL (Nucleolin) exhibited a perfectly segregating in-frame deletion (ENST00000322723.4:p.Asp261del). NCL is involved in the synthesis and maturation of ribosomes and is LoF intolerant (pLI = 1) ⁵⁰. This gene is highly expressed in brain (Table S3). In a GWAS study, NCL was associated with ADHD ⁵¹. Disrupting the function of NCL causes nucleolar stress, which is believed to be one of the pathogenic mechanisms for neurodegenerative diseases including polyglutamine (polyQ) diseases and Parkinson’s diseases ^{52, 53}. In Huntington disease, the polyQ mutation in the HTT gene functionally disrupts the normal ribosomal interaction with NCL ⁵⁴.

FAM4

Pedigree FAM4 includes a single sperm donor, six mothers, and nine offspring, among which eight are TD cases. As four of the mothers were unaffected, it is highly likely that there are one or a few dominant mutation(s) from the sperm donor which were passed to the affected children. In the pVAAST dominant run, 10 genes remained after filtering. Among those genes, DCAF1 (DDB1 And CUL4 Associated Factor 1, also known as VPRBP) contains a perfectly segregating variant. DCAF1 is a Serine/threonine-protein kinase which is also a component of E3 ubiquitin-protein ligase complexes and plays key roles in various biological processes including cell cycle, telomerase regulation, and histone modification ⁵⁵.

Besides DCAF1, several other genes also contain variants with one genotype/ phenotype discrepancy (Table 2). ZDHHC17 (Zinc Finger DHHC-Type Palmitoyltransferase 17) contains a rare nonsynonymous variant (ENST00000426126.2:p.Ile582Thr), which is predicted to be possibly damaging by Polyphen-2. The variant is present in the sperm donor, five affected children, and absent in one affected child and all unaffected individuals. ZDHHC17 was previously named Huntingtin interacting protein 14, based on its interaction with the Huntington disease gene, HTT. It is a membrane protein with palmitoyl-transferase activity which is specific for several critical neuronal proteins including HTT ⁵⁶ and GRIN2B (Glutamate Ionotropic Receptor NMDA Type Subunit 2B) ⁵⁷. A mouse model for Huntington disease, Zdhhc17^{Gt(RRJ233)Byg}/Zdhhc17^{Gt(RRJ233)Byg} shows a series of phenotypes in the nervous system and behavior/neurological changes, including increased neuronal apoptosis, decreased brain weight, abnormal striatum morphology, and hyperactivity. ZDHHC17 showed significant interactions with genes in TD_simplex, ADHD, and ASD_high lists (Table S3), suggesting it plays important roles in multiple NDDs.

Other small families

Because smaller families contain less information in variant segregation, these families typically present more candidate genes than larger ones and it is more difficult to rank the genes. However, several candidate genes are known to associate with other NDDs or were identified in previous TD genetic studies (Table 2). For example, CNTNAP5 (Contactin Associated Protein Family Member 5) in FAM11 is a known NDD gene and variants in CNTNAP5 have been reported to be associated with ASD and other NDDs (https://gene.sfari.org/database/human-gene/CNTNAP5). ANK3 (ankyrin 3) in FAM8 is a high-confidence ASD candidate gene and has also been reported in TD_simplex family studies ¹⁸. The overlap between our top TD_multiplex family genes with NDD candidate genes suggest a shared genetic etiology for TD and other NDDs.

Individual family CNV analysis

For nine families, we also obtained CNVs using genotyping array (Table 1). A total of 1,799 high quality CNVs were identified, with an average of 200 CNVs per family (ranging from 116 to 289). The majority of the CNVs were unique to one individual. After filtering on the segregation pattern, gene overlap, and the CNV size (See Methods for filtering detail), we obtained 22 genic CNVs, including 16 exonic CNVs (Table S5). Risk genes predicted by CNVs did not overlap candidate genes from the SNV analysis (Table 3). We further prioritized the 22 CNVs based on their population prevalence (AF <1%), encompassed candidate gene brain expression level, and candidate gene function. After prioritization, three candidate CNVs remained. The first CNV is a heterozygous deletion in FAM1 (g.chr5:73980065–73992881) overlapping the first several exons of HEXB (Hexosaminidase Subunit Beta). This deletion was perfectly segregating with the phenotype and the genomic region only contains two rare CNVs in human populations in the gnomAD structure variation database (https://gnomad.broadinstitute.org/region/5–73980065-73992881?dataset=gnomad_sv_r2_1). HEXB encodes a subunit of the lysosomal enzyme beta-hexosaminidase, and mutations in HEXB has been associated with neurodegenerative Sandhoff disease ⁵⁸. The second CNV is a heterozygous deletion in FAM9 (g.chr20:5281253–5289644). The deletion is rare in populations and overlaps the last exon of gene PROKR2 (Prokineticin Receptor 2). PROKR2 has increased brain expression and has been associated with Kallman syndrome ⁵⁹. The third CNV, from FAM5, is in the intron of ZNF385D (Zinc Finger Protein 385D). This heterozygous deletion segregated in five out of eight affected individuals in the family and is rare in general populations. ZNF385D has been associated with other neuropsychiatric disorders, such as schizophrenia ⁶⁰ and reading disability ⁶¹, supporting ZNF385D as a candidate gene for TD.

Pathways implicated in multiple families

Because of the apparently high genetic heterogeneity of TD, it is likely that each multiplex family have different underlying genetic risk factors. Although only 25 of the 543 TD_multiplex genes were present in more than one family, the families could share the underlying TD genetic etiology beyond the single gene level, such as pathways and gene groups. In support of the latter, a polygenic risk score has been associated with TD ¹². Therefore, we examined the interaction for the top 71 candidate genes (Table 2), as well as their interactions with other NDD genes. Our result showed strong interactions among our top candidate genes: 35 of the 71 genes showed interactions and form a single PPI network (Figure 3A). Many of the remaining genes showed extensive interaction with other NDD genes (Figure 3B), suggesting the shared genetic etiology among NDDs. Interestingly, submodules emerging from the PPI network include potassium voltage-gated channel genes, and axonemal dynein genes (Figure 3B).

Figure 3. Protein-protein interaction (PPI) networks.

(A) PPI network of the 71 TD top candidate genes. Only genes that can be connected are shown. (B) PPI networks of the 71 TD top candidate genes not in (A). Other NDD_all genes were added as intermediate nodes if they interact with more than one TD top candidate genes. For intermediate nodes, only interactions with top candidate genes were included. (C) PPI network formed by NDD_all genes identified in axoneme (GO:0005930). (D) PPI networks formed by NDD_all genes in synaptic membrane (GO:0097060). To simplify the network, interactions between non-candidate genes were removed. PPI networks were defined by three databases, ConsensusPathDB, STRING, and GIANT_v2. Genes were colored by the gene lists (see Methods for details).

To further explore relationships among TD candidate genes and to identify potential shared mechanisms between TD and different NDDs, we performed enrichment analysis for all 2,345 NDD genes (NDD_all, Table S1) using ConsensusPathDB, and calculated the significance of enrichment for each gene list using Fisher’s exact test (Table S6). NDD_all, TD_multiplex, as well as several other gene lists were highly enriched in GO terms and pathways related to brain function and developmental processes, highlighting that many genes in this group are involved in brain development (Table S6). Among GO terms enriched in TS_multiplex gene list, “dynein light intermediate chain” (GO:0051959, 29 genes) is the top molecular function term and “axoneme part” (GO:0044447, 37 genes) is the top cellular component term. Seven genes from five multiplex families are defined under the dynein light intermediate chain term, including DNAH3, DNAH5, DNAH7, DNAH11, RAB11FIP3, DYNC2H1, and CCDC88B. Interestingly, four genes in the TD_simplex gene set (DNAH5, DNAH6, DNAH10, and RAB11FIP3) and four genes in the ASD gene set (DNAH3, DNAH10, DNAH17, and CCDC88C) are also present in the “dynein light intermediate chain” gene set and the NDD_all genes showed significant enrichment for the term as well (q = 5.1×10⁻⁴, Table S6). These genes encode dynein motor proteins that are parts of the cellular cytoskeleton and showed extensive interaction in the PPI network (Figure 3C). The overlapping candidate genes within the dynein complex in both TD and other NDDs suggests that microtubule function might be an important factor for NDDs and TD in particular. In the enrichment analysis of all NDD genes, the most enriched GO terms included synaptic vesicle cycle and presynaptic membrane (hypergeometric test, q = 9.2×10⁻²³ and 2.7×10⁻²⁰, respectively, Table S6). Most of these genes were also under the GO term “synaptic membrane” and showed extensive interaction in the PPI network (Fig. 3D). In addition to their enrichment in overall gene list, genes in the “synaptic vesicle cycle” and “presynaptic membrane” GO terms were also enriched in most individual gene lists (excluding TD_CNV and OCD), indicating the importance of genes in these two categories in multiple NDDs.

Discussion

TD is a highly heritable and frequently occurring neurodevelopmental disorder that can cause significant burden in patients and families ¹⁴. As a complex disease with heterogeneous etiology, it is difficult to study its mechanism with classic candidate gene methods and only a few genes have been reported from single gene studies (Reviewed in ¹⁵). Multiplex families provide additional information compared to simplex trio studies: as multiple affected individuals are identified in a single family, the variant segregation pattern within the family can help identify inherited rare variants with strong effects. In this study, we examined 13 multiplex families to identify potential TD candidate genes.

Among our multiplex families, the co-occurrence of OCD and ADHD is consistent with previous studies ^{3–5, 7–9}: a total of 37.8% (31/82) of individuals with TD and other tic disorders were also diagnosed with OCD, and 20.7% (17/82) had comorbid ADHD (Table S2). On the other hand, the sex ratio between male and female is 1.3, smaller than the ratio in population studies where boys are affected three to five times more often than girls ¹. It has been proposed that the lower affectation rate in girls could be explained by a “female protective effect” which protects females from many variants with small effects. This increased rate of affected females in multiplex families may be consistent with the segregation of large-effect variants that are needed to affect females in these families.

Our initial analysis of the 13 families identified hundreds of candidate genes across the genome. The large number of candidate genes could be due to both the complex genetic etiology of TD and the lack of segregation information in small families. Therefore, to facilitate candidate gene prioritization, we considered multiple types of information for variants and genes, including mutation functional impact prediction, segregation pattern, gene function, gene known disease association(s), and gene expression patterns in brain (Figure 2). By combining the evidence, we prioritized candidate risk genes and generated a top candidate gene list of 71 genes. We also found three segregating genic CNVs that could also contribute to TD etiology in three families. Some of the top candidate genes are well known NDD genes, such as GRIN2B, CNTNAP5, GABRB3, and ANK3. For four patients with diagnosis for ADHD, their exomes contain variants in reported genes associated with ADHD (SLC6A2, ANK3). We believe these genes are also TD risk genes in these families because the variants showed strong segregating patterns with TD patients. Fourteen genes contained likely damaging de novo mutations in previous studies of TD simplex trios (Table S4), such as BCAN, DNAH5, and RAB11FIP3, providing additional support for their involvement in TD etiology. More importantly, we identified several novel candidate genes in the multiplex families that have not been previously linked to TD or NDDs. For example, TADA3 is the single candidate gene in FAM5. The evidence from this study warrants close examination of these genes in future TD studies.

The candidate genes also allowed us to identify potential pathways and biological processes that are important for TD etiology. One of the strongest signals in our dataset is associated with the components of dynein pathways, the motor proteins for cellular cytoskeleton (Figure 3C). Dyneins are important for protein transport within neurons, especially for signaling proteins that function in axons that are far distal to the cell body ⁶². This result suggests that the cytoskeleton might play an important role in TD development. One interesting observation is that although the GO term and pathway enrichments of many NDD gene lists are related to neuronal and brain function, many enrichments in TD_multiplex and TD_simplex gene lists are different from enrichments in other NDD lists (Table S6). This suggests that TD might have distinct genetic risk factors compared to other NDDs. We also observed shared enrichment in molecular functions and biological pathways among all NDD genes. For all NDD genes, the most enriched GO terms and pathways showed strong relationships with brain functions, such as presynaptic membrane, synaptic vesicle cycle, neurotransmitter secretion, dopamine metabolic process, glutamate receptor signaling pathway, and GABAergic synapse (Table S6). Therefore, studies from other NDDs may also contribute to understanding the genetic bases of TD.

As a complex disorder with genetic heterogeneity ¹⁸, risk genes in TD are likely to work together in networks. Our PPI network analysis supports this hypothesis: many PPIs were predicted within the top TD candidate genes and with genes from other NDD lists by the three PPI databases (Fig. 3). For these disorders, some of the most enriched terms are related to the proper function of neurons, such as synaptic membrane, synaptic vesicle cycle, and voltage−gated ion channel activity.

In summary, by leveraging the segregation information in multiplex families, the power of whole-exome sequencing and genome-wide CNV analysis, we identified new candidate genes as well as biological processes and pathways that could contribute to the development of TD. Future studies of TD with larger sample sizes are needed to provide further support for the candidate genes. Furthermore, functional studies of these candidate genes, either in cell-culture systems or animal models, will provide a better understanding of their roles in TD, eventually leading to improved diagnosis and treatment.

TIC Genetics author list (in alphabetical order)

Mohamed Abdulkadir^1,2,3, Lawrence W. Brown¹², Xiaolong Cao¹, Barbara J. Coffey¹³, Li Deng^1,2, Andrea Dietrich³, Thomas V. Fernandez^4,5, Blanca Garcia-Delgar⁶, Donald L. Gilbert¹⁴, Julie Hagstrøm⁷, Tammy Hedderly¹⁵, Gary A. Heiman^1,2, Isobel Heyman¹⁶, Pieter J. Hoekstra³, Chaim Huyser¹⁷, Eunjoo Kim¹⁸, Young-Shin Kim¹⁹, Robert A. King⁴, Justin Koesterich^1,2, Yun-Joo Koh²⁰, Samuel Kuperman⁸, Bennett L. Leventhal²¹, Marcos Madruga-Garrido²², Athanasios Maras²³, Pablo Mir^24,25, Astrid Morer^6,9,10, Alexander Münchau²⁶, Cara Nasello^1,2, Kerstin J. Plessen^7,11, Veit Roessner²⁷, Dong-Ho Song²⁸, Matthew W. State¹³, Joshua K. Thackray^1,2, Jay A. Tischfield^1,2, A. Jeremy Willsey^13,29, Jinchuan Xing^1,2, Yeting Zhang^1,2, Lisheng Zhou¹, and Samuel H. Zinner³⁰.

¹ Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

² Human Genetic Institute of New Jersey, Rutgers, the State University of New Jersey, Piscataway, NJ, USA

³ Department of Child and Adolescent Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

⁴ Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA

⁵ Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

⁶ Department of Child and Adolescent Psychiatry, and Psychology, Institute of Neurosciences, Hospital Clinic, Universitari, Barcelona, Spain

⁷ Child and Adolescent Mental Health Center, Mental Health Services, Capital Region of Denmark, Denmark

⁸ Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA

⁹ Institut d’Investigacions Biomediques August Pi i Sunyer, (IDIPABS), Barcelona, Spain

¹⁰ Centro de Investigacion en Red de Salud Mental (CIBERSAM), Instituto Carlos III, Spain

¹¹ Division of Child and Adolescent Psychiatry, University Hospital Lausanne, Switzerland

¹²Pediatric Neuropsychiatry Program, Division of Neurology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA

¹³Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA

¹⁴Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

¹⁵Evelina London Children’s Hospital GSTT, Kings Health Partners AHSC, London, UK.

¹⁶Tic Disorder Clinic, Great Ormond Street Hospital for Children NHS Trust, London, WC1N 3JH, UK

¹⁷Levvel, specialists for Youth and Families, Amsterdam, 1015 AZ, The Netherlands.

¹⁸Department of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

¹⁹Department of Psychiatry & Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.

²⁰Korea Institute for Children’s Social Development and Rudolph Child Research Center, Seoul, South Korea

²¹University of California San Francisco Medical Center, San Francisco, CA, 94143, USA.

²²Sección de Neuropediatría, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain

²³Yulius Academy, Yulius Mental Health Organization, Dordrecht, 3311 JG, The Netherlands

²⁴Unidad de Trastornos del Movimiento. Instituto de Biomedicina de Sevilla (IBiS). Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla. Seville, Spain

²⁵Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain

²⁶Institute of Systems Motor Science; University of Lübeck, Lübeck, Germany

²⁷Department of Child and Adolescent Psychiatry, TU Dresden, Germany

²⁸Yonsei University Severance Hospital, Seoul, South Korea

²⁹Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

³⁰Department of Pediatrics, Division of Developmental Medicine, University of Washington School of Medicine, Seattle, WA, USA