Diagnostic Genetic Findings From Exome Sequencing in a Cohort of 1,109 Children With Epilepsy

Abstract

Background and Objectives

Genetic testing has emerged as a transformative tool for the diagnosis and treatment of epilepsy. The aim of this study was to characterize the genetic basis of pediatric epilepsy.

Methods

We analyzed a cohort of 1,109 children with epilepsy who underwent whole-exome sequencing. Genetic findings were interpreted based on medical records and genetic testing results.

Results

Genetic diagnostic results were found in 405 of 1,109 patients, with a diagnostic yield of 36.5%. The SCN1A (40/1,109, 3.6%) was the most frequently affected gene, followed by the PRRT2 (26/1,109, 2.3%) and SCN2A (10/1,109, 0.9%). In total, 138 genes were identified with 337 total detections. Gene ontology analysis revealed enrichment in ion channel–related genes (30.0%, 101/337), catalytic activity–related genes (19.6%, 66/337), and pathway-related genes (14.5%, 49/337). Multivariate logistic regression showed that younger age at onset (OR = 0.87, 95% CI 0.81–0.94, p < 0.001), developmental delay or intellectual disability (OR = 2.25, 95% CI 1.62–3.12, p < 0.001), and facial dysmorphisms (OR = 2.30, 95% CI 1.06–5.00, p = 0.036) were associated with a higher likelihood of achieving a genetic diagnosis. Negative results were obtained in 51.4% (570/1,109) of patients.

Discussion

This single-center study provides a comprehensive overview of the genetic landscape of pediatric epilepsy, enhancing our understanding of the genetic basis and offering insights for clinical diagnosis and genetic counseling. These findings underscore the clinical utility of genetic testing in pediatric epilepsy.

Introduction

Epilepsy is a chronic neurologic disorder characterized by recurrent, unprovoked seizures resulting from abnormal neuronal activity in the brain.¹ It is reported that approximately 50 million people worldwide are affected by epilepsy.² In recent years, the genetic basis of epilepsy has been increasingly recognized, with genetic etiologies identified in up to 40% of patients.³ In fact, the absence of detectable variants does not exclude a genetic cause. Genetic factors may play a more significant role in epilepsy than previously thought.

Whole-exome sequencing (WES) has proven to be a transformative tool, enabling the discovery of the genetic basis of diseases at the molecular level. In the field of epilepsy, the application of WES, combined with chromosomal microarray analysis, facilitates the detection of single nucleotide variants (SNVs), small insertions or deletions (indels), and copy number variants (CNVs), thereby supporting the development of etiology-based antiepileptic treatment strategies. In addition, WES has been essential in advancing genetic research into epilepsy etiology. A large number of epilepsy genes have been identified,⁴ especially through familial transmission studies using WES.^5,6

Despite the advancements WES has brought to clinical management and genetic research in epilepsy, there are several challenges. For instance, the genetic basis of epilepsy, especially within Chinese populations, still requires further investigation. While large-scale genetic studies of epilepsy have primarily focused on American and European cohorts,^7-10 data on Chinese patients with epilepsy are limited, in both the number of studies and the size of the cohorts. In addition, the use of different genetic testing methods within the same study can introduce heterogeneity in the results.^11-13

In this study, we outline a genetic variation landscape of pediatric epilepsy based on a single-center cohort, consisting of 1,109 patients who underwent WES. Our analysis represents an advancement in characterizing the genetic basis of pediatric epilepsy in China and provides resources for further genetic research.

Methods

Study Population

This retrospective cohort study included patients from Capital Institute of Pediatrics (Beijing, China) during the period from January 2017 to October 2023. The inclusion criteria were as follows: (1) seizure onset before the age of 18, (2) meeting the diagnostic criteria for epilepsy according to the International League Against Epilepsy criteria,¹⁴ and (3) underwent WES at our center. Patients were excluded if they had only provoked seizures, such as simple febrile seizures without evidence of epilepsy, or if their seizures were caused by acquired brain injuries, such as perinatal brain injury or head trauma. We did not exclude patients with malformation of cortical development, developmental brain tumors, and nonspecific brain MRI findings. The assessment team consisted of 2 pediatric neurologists and one genetic analyst, and genetic diagnosis was confirmed to be consistent with the clinical phenotype. The evaluation included medical history, clinical phenotypes, electroencephalography (EEG), brain neuroimaging including MRI and CT, and genotype-phenotype correlations.

Exome Sequencing and Variant Interpretation

WES was performed in all patients. Trio WES involved analyzing the proband and both biological parents as a single analytical unit, and proband-only sequencing encompassed cases where only the proband underwent WES.

In most proband-only cases, the origin of identified variants was confirmed using biological parent samples. SNV and indel detection as well as WES-based CNV calling was performed through WES. In this study, indels refer to small insertions or deletions in the nucleotide sequence, with a length of less than 50 bp. In addition to WES, genotyping array was performed in 887 patients to detect CNVs and mitochondrial gene sequencing was performed in 165 patients. Sample processing, DNA sequencing, and variant detection methods are detailed in eMethods.

Pathogenicity classification was conducted in accordance with the American College of Medical Genetics and Genomics guidelines for the interpretation of variants,^15,16 and the variant classification categories included pathogenic (P), likely pathogenic (LP), variants of uncertain significance (VUS), likely benign, and benign. Given that pathogenicity classifications may change with the accumulation of evidence, we conducted a re-evaluation of the pathogenicity for all variants in November 2023 to ensure consistent classification. A genetic diagnostic result was defined as the presence of variant genes or CNVs consistent with the patient's phenotype, with all variants classified as P/LP. Patients with diagnostic variants at a single locus were classified as having a single molecular diagnosis, whereas those with diagnostic variants at more than one locus were classified as having multiple (dual or triple) diagnoses. A single CNV event, even if spanning multiple genes, was considered one molecular diagnosis. Negative results were defined as the absence of variants associated with the phenotype.

Genes identified in the diagnostic results were subjected to gene ontology (GO) enrichment analysis. Because a gene may participate in multiple biological processes, we classified genes according to their biological functions listed in the Gene Ontology Resource database¹⁷ and relevant literature. Two researchers independently assigned each gene to the biological process most likely involved in epilepsy pathogenesis, and any discrepancies were resolved by the corresponding author.

Statistical Analysis and Software

Continuous variables were compared using logistic regression analysis. Proportions were presented as percentages, and group comparisons were conducted using the χ² test. Data analysis and visualization were performed with SPSS (version 26.0) and R (version 4.3.1).

Standard Protocol Approvals, Registrations, and Patient Consents

The study was approved by the Institutional Review Board of the Capital Institute of Pediatrics (SHERLL2022064). Written informed consent was obtained from all parents or legal guardians and from probands capable of providing it.

Data Availability

The deidentified data of this study are available from the corresponding author on reasonable request.

Results

Demographics and Clinical Data of Study Population

Overall, the study included a total of 1,109 patients. There were 644 male patients and 465 female patients, and the male-to-female ratio was 1.4:1. The median age at seizure onset was 1.5 years, with a quartile range of 0.5–3.6 years, and the median age at genetic testing was 2.6 years. The possibility of obtaining a diagnosis decreased with age increasing (p < 0.001). Among the 1,109 patients, 975 (87.9%) underwent trio WES, while 134 (12.1%) underwent proband-only WES. Focal seizures (46.7%, 518/1,109) were the most common seizure type. Many patients were diagnosed with specific epileptic syndromes, including 123 with infantile epileptic spasms syndrome (IESS), 46 with genetic epilepsy with febrile seizures plus (GEFS+), 19 with early-infantile developmental and epileptic encephalopathy (EIDEE), 14 with childhood absence epilepsy (CAE), 13 with Dravet syndrome, 11 with febrile seizures plus (FS+), 10 with self-limited epilepsy with centrotemporal spikes (SeLECTS), 8 with epilepsy with myoclonic-atonic seizures (EMAtS), 8 with epileptic encephalopathy/developmental epileptic encephalopathy with spike-and-wave activation in sleep (EE/DEE-SWAS), and 6 with Lennox-Gastaut syndrome (LGS). Many patients had other phenotypes, including 432 patients with developmental delay/intellectual disability (DD/ID), 44 with facial dysmorphism, and 119 with dystonia. Furthermore, neuroimaging identified malformation of cortical development or developmental brain tumor in 61 patients. Demographic characteristics and clinical data of the study population are summarized in Table 1 and eTable 1.

Table 1.

Demographic Characteristics and Clinical Phenotypes of Patients

Variables	No. of patients	Diagnostic yield, number (%)c	p Value
Total	1,109	405 (36.5)	—
Sex			0.055
Female	465	185 (39.8)
Male	644	220 (34.2)
Age at seizure onset			<0.001d
Neonatal (0–1 mo)	75	41 (54.7)
Infancy (1 mo–1 y)	396	182 (46.0)
Toddler (1–3 y)	287	90 (31.4)
Early childhood (3–6 y)	179	44 (24.6)
Middle childhood (6–12 y)	102	32 (31.4)
Adolescent (12–18 y)	11	2 (18.2)
Not availablea	59	14
Family structure			0.058
Trio	975	366 (37.5)
Proband-only	134	39 (29.1)
Seizure types			0.002e
Generalized	262	78 (29.8)
Focal	518	218 (42.1)
Generalized and focal	56	17 (30.4)
Other phenotypes
Premature	66	20 (30.3)	0.283
Febrile seizure history	240	81 (33.8)	0.352
DD/ID	432	222 (51.4)	<0.001f
Facial dysmorphisms	44	27 (61.4)	0.001f
Dystonia	119	61 (51.3)	<0.001f
Micro/macrocephaly	104	55 (52.9)	<0.001f
Family history of epilepsy	77	29 (37.7)	0.912
Neuroimaging			0.396
Specific abnormalityb	61	20 (32.8)
Non-specific abnormality	294	117 (39.8)
Normal	632	226 (35.8)
Not available	122	42

Abbreviations: DD = developmental delay; ID = intellectual disability.

^aAlthough the exact ages were not available for some patients, all individuals in this group were confirmed to be younger than 18 y as the cohort was derived entirely from a pediatric hospital.

^bA specific abnormality included malformation of cortical development and developmental brain tumor.

^cPercentages indicate the proportion of patients with a genetic diagnosis within each group.

^dDiagnostic results were more likely with earlier onset.

^ePost-test showed significance between the generalized group and focal group, indicating that patients with focal seizures were more likely to receive a genetic diagnosis than those with generalized seizures.

^fDiagnostic results were more likely with DD/ID, facial dysmorphisms, dystonia, and micro/macrocephaly.

Genetic Diagnostic Results

The summary of genetic testing results is presented in Figure 1. Overall, diagnostic results were reported in 405 patients, with a diagnostic yield of 36.5%. Among these 405 patients, 325 (80.2%) had SNV/indels, including 318 (78.5%) with SNV/indels alone and 7 (1.7%) with both SNV/indels and CNVs (Figure 2). In addition, 87 patients (21.5%) had CNVs (Figure 2), including 77 with multigenic CNVs and 10 with intragenic CNVs.

Figure 1. Summary of Genetic Testing Results From Patients With Epilepsy.

Figure 2. Distribution of Diagnostic Variant Types and Inheritance Modes.

The left panel shows the proportion of different variant types among the 405 patients with genetic diagnoses. The bar plots summarize the types of SNV/indels identified (upper bar plot) and the inheritance modes of variants identified in patients with a single molecular diagnosis (lower bar plot; patients with dual/triple molecular diagnoses are summarized in eTable 2). All variants included were classified as pathogenic or likely pathogenic. AD = autosomal dominant; AR = autosomal recessive.

Among the 325 patients with diagnostic SNV/indels, 155 (47.7%) had missense variants, 77 (23.7%) had frameshift variants, 44 (13.5%) had nonsense variants, 17 (5.2%) had splicing variants, 12 (3.7%) had other variants (6 patients with small deletions, 4 with mitochondrial tRNA variants, 1 with small duplication, and 1 with synonymous variant), and 20 (6.2%) had 2 or more types of SNV/indel variants (Figure 2). Among the 405 patients with diagnostic results, 393 had a single molecular diagnosis and 12 had dual or triple molecular diagnoses. Among the 393 patients with a single molecular diagnosis, 250 (63.6%) had AD-inherited variants, 38 (9.7%) had XL-inherited variants, 29 (7.4%) had AR-inherited variants, 6 (1.5%) had mitochondrial variants, and 70 (17.8%) had multigenic CNVs (Figure 2). Among the 12 patients with dual or triple molecular diagnoses, the diagnostic findings included the following categories: 4 patients carried AD-inherited variants (eTable 2, patients 1–4), 4 carried both AD-inherited variants and multigenic CNVs (eTable 2, patients 5–8), 3 carried multigenic CNVs only (eTable 2, patients 9–11), and one patient carried both AD-inherited and AR-inherited variants affecting different genes (eTable 2, patient 12). All variants described above were classified as P/LP.

The top 10 genes contributing to diagnostic findings are listed in Table 2. The SCN1A and PRRT2 were the most frequently affected genes, with the diagnostic yield of 3.6% and 2.3%, respectively. The most frequently recurrent P/LP variant was PRRT2 (NM_145239.2): c.649dup, observed in 80.8% (21/26) of cases with diagnostic PRRT2 variants and in 5.2% (21/405) of all diagnostic cases. Furthermore, in the 26 patients with PRRT2 gene variants, 25 had variants located on c.649C, reported as a hotspot of PRRT2 variants.¹⁸ The genetic diagnostic results of epilepsy syndromes are summarized in Table 3. The highest diagnostic yield was observed in Dravet syndrome, with all 13 patients receiving a genetic diagnosis and all carrying SCN1A variants. This was followed by EIDEE, in which 16 of 19 patients (84.2%) had a genetic diagnosis, most frequently involving KCNQ2 variants.

Table 2.

Top 10 Genes Contributing to Diagnostic Results

Gene	Chromosome	Inheritance mode	Diagnostic yield, number (%)a
SCN1A	2	AD	40 (3.6)
PRRT2	16	AD	26 (2.3)
SCN2A	2	AD	10 (0.9)
KCNQ2	20	AD	9 (0.8)
SCN8A	12	AD	9 (0.8)
DEPDC5	22	AD	8 (0.7)
CDKL5	X	XL	8 (0.7)
STXBP1	9	AD	7 (0.6)
MMACHC	1	AR	7 (0.6)
PCDH19	X	XL	7 (0.6)

Abbreviations: AD = autosomal dominant; AR = autosomal recessive; XL = X-linked.

^aPercentages indicate the proportion of patients with a genetic diagnosis among the 1,109 patients.

Table 3.

Diagnostic Genetic Results With the Epilepsy Syndromes

Epilepsy syndrome (no. of cases)	Diagnostic yield, number (%)a	SNV/indel (no. of cases)	CNV (no. of cases)c
IESS (123)	39 (31.7%)	STXBP1 (2), CDKL5 (2), SCN8A (2), GPT2 (1), MTHFR (1), RARS2 (1), CDH2 (1), CUX2 (1), DEPDC5 (1), DNM1 (1), IQSEC2 (1), IRF2BPL (1), KCNQ2 (1), KMT2E (1), MT-ATP6 (1), MTOR (1), NACC1 (1), NF1 (1), NR2F1 (1), NSD1 (1), LIS1 (1), PHACTR1 (1), PHF21A (1), POU3F3 (1), PRRT2 (1), SCN2A (1), TSC2 (1), TUBA1A (1), TUBG1 (1), WDR45 (1)	1p36.33p36.32del (2), 15q11.2del (1), 16p13.11del (1), 19p13.3del (1), 8p23.1p12dup (1), 8p23.3p23.1del (1), 9q34.11del (1), Xq26.3q28del (1)
GEFS+ (46)	14 (30.4)	SCN1A (6), PRRT2 (2), ANKRD11 (1), FOXP1 (1), GABRB3 (1), POGZ (1)	15q11.2del (2), 16p11.2del (1)
EIDEE (19)	16 (84.2)	KCNQ2 (4), CDKL5 (2), ACADS (1), ALDH7A1b (1), HCN1 (1), KCNT1 (1), PTEN (1), SCN2A (1), SLC2A1 (1), SLC35A2 (1), STXBP1 (1), ZMYND11 (1)	2q24.3del (1), ALDH7A1 ex.6delb (1)
CAE (14)	3 (21.4)	KIF11 (1), SETD1B (1), SCN8A (1)	—
DS (13)	13 (100.0)	SCN1A (13)	—
FS+ (11)	3 (27.3)	SCN1A (2), KCNA2 (1)	—
SeLECTS (10)	1 (10.0)	KDM5C (1)	—
EMAtS (8)	2 (25.0)	MECP2 (1)	15q11.2del (1)
EE/DEE-SWAS (8)	2 (25.0)	KAT6A (1), GRIN2A (1)	—
LGS (6)	1 (16.7)	STXBP1 (1)	17q11.2del (1)

Abbreviations: CAE = childhood absence epilepsy; DS = Dravet syndrome; EE/DEE-SWAS = epileptic encephalopathy/developmental epileptic encephalopathy with spike-and-wave activation in sleep; EIDEE = early-infantile developmental and epileptic encephalopathy; EMAtS = epilepsy with myoclonic-atonic seizures; FS+ = febrile seizure plus; GEFS+ = genetic epilepsy with febrile seizures plus; IESS = infantile epileptic spasms syndrome; LGS = Lennox-Gastaut syndrome; SeLECTS = self-limited epilepsy with centrotemporal spikes.

^aPercentages indicate the proportion of patients with a genetic diagnosis of each epilepsy syndrome.

^bSNV/indel and CNV, respectively, constitute 2 allelic variants of autosomal recessive diseases.

^cThe evaluation of CNVs was performed according to the ACMG standards,¹⁶ and each CNV was either previously reported as being associated with epilepsy or involved at least one epilepsy-related gene. Recurrent CNVs are detailed in eTable 3.

There were 87 patients with diagnostic CNVs, among whom 64 (73.6%) had deletions, 20 (23.0%) had duplications, and 3 (3.4%) carried both deletions and duplications. The CNV distributions on chromosomes according to the number of variants are presented in eFigure 1, and the recurrent diagnostic CNVs are summarized in eTable 3. While diagnostic variants were most frequently located on chromosome 2, chromosome 16 had the highest frequency (17/87, 19.5%) of diagnostic CNVs, followed by chromosome 15 (16/87, 18.4%). Two patients had karyotypic abnormalities, including one case of 47, XXX, and one case of 48, XXXY. We did not perform a separate analysis of the number of diagnostic CNVs identified through genotyping array because all CNVs detected in the cohort could be identified using WES-based CNV calling.

In the cohort, mitochondrial sequencing was conducted in 165 patients. Four of them had the MT-TL1(m.3243A > G) variants: 2 were maternally inherited with a lower variant ratio than the proband, one was de novo, and one had an uncertain source because of the unavailable sample from the biological mother. One had the MT-ATP6 (m.8993T > G) variant, maternally inherited with a lower variant ratio than the proband, and one had a de novo MT-ND6 (m.14453G > A) variant. Detailed information of mtDNA findings is summarized in eTable 4.

The diagnostic genes were subjected to GO enrichment analysis. A total of 138 genes were identified, with 337 total detections. Among them, ion channel–related genes were the most frequently identified, accounting for 30.0% (101/337), followed by genes related to catalytic activity (19.6%, 66/337) and pathway-related genes (i.e., those involved in signaling or biochemical pathways) (14.5%, 49/337). Among ion channel–related genes, those associated with sodium ion channel complexes were the most common (58.4%, 59/101). Within catalytic activity–related genes, the most common were kinase genes, accounting for 24.2% (16/66), and within pathway-related genes, the most common were those related to the mammalian target of rapamycin (mTOR) signaling pathway, comprising 46.9% (23/49).

Factors Influencing Diagnostic Results of WES

We analyzed the factors influencing the acquisition of genetic diagnoses in univariate logistic regression analysis (eFigure 2). Younger age at onset (OR = 0.89, 95% CI 0.84–0.93, p < 0.001), facial dysmorphisms (OR = 2.76, 95% CI 1.48–5.14, p = 0.001), dystonia (OR = 1.97, 95% CI 1.33–2.91, p < 0.001), abnormal head circumference (OR = 2.10, 95% CI 1.39–3.17, p < 0.001), and the presence of DD or ID (OR = 3.14, 95% CI 2.41–4.08, p < 0.001) were associated with a higher likelihood of obtaining a genetic diagnosis. We then constructed a multivariate logistic regression model, which revealed that a younger age at onset (OR = 0.87, 95% CI 0.81–0.94, p < 0.001), the presence of DD/ID (OR = 2.25, 95% CI 1.62–3.12, p < 0.001), and facial dysmorphisms (OR = 2.30, 95% CI 1.06–5.00, p = 0.036) were associated with a higher likelihood of obtaining a genetic diagnosis.

Summary of Patients With Variants of Unknown Significance and Negative Results

There were 134 patients with variants of unknown significance, with missense variants being the most common (105/134, 78.4%). The most frequent gene identified was SCN1A (10/134, 7.5%), followed by SCN2A (6/134, 4.5%) and PRRT2 (4/134, 3.0%); these genes also ranked among the top 3 in diagnostic results. Further functional studies by geneticists or additional clinical cases are needed to provide evidence for reclassifying variants of unknown significance. There were 570 patients (51.4%) with negative results in the cohort.

Discussion

In this study, we report genetic testing results from a single-center pediatric cohort of 1,109 Chinese children with epilepsy. This study contributes to the expanding body of literature in the field, providing insights for clinical diagnosis and genetic counseling.

In our cohort, the male-to-female ratio was 1.4:1, potentially reflecting a higher prevalence of epilepsy in male patients compared with female patients.^19,20 Consistent with previous studies, the diagnostic rate showed a decline with increasing age.^11,21 The overall diagnostic yield for our cohort was 36.5%. A meta-analysis in 2021 reported a genetic diagnostic yield of 24.0%, pooling data from 72 studies focusing on genetic results in patients with epilepsy.²¹ Concerning WES, studies revealed a diagnostic range from 30.6% to 42.0% in patients with epilepsy.^12,13,22,23 This broad range could be attributed to various factors. First, different centers used varied analytical approaches to implement genetic testing, with some integrating NGS-based CNV sequencing, and in our study, screening for mitochondrial genes was also performed in patients suspected of mitochondrial diseases. Second, patient selection was often nonrandomized, influenced by subjective judgment of clinicians, as well as the level of disease awareness among patients and their families. Third, heterogeneity within patient populations, including geographic or ethnic diversity, age spectrum, and clinical phenotypic variances, could affect genetic outcomes. Finally, reporting disparities or publication bias across studies might contribute to the observed wide range in diagnostic yields. In fact, the annual diagnostic rates in our study also fluctuated, which may be related to the variations in disease composition and the choice of sequencing strategy. Of note, the diagnostic yield was similar to the 36.6% observed in WGS performed in 320 Chinese children with epilepsy.¹¹

In this study, SCN1A was the most frequently detected, followed by PRRT2. Previous studies also identified SCN1A as the most common epilepsy gene.^7,8,10,24 In these studies, the second most common genes were KCNQ2, MECP2, or STXBP1, instead of PRRT2 identified in this cohort. Consistently, some studies in China also found PRRT2 as the second most frequently detected gene, sometimes even surpassing SCN1A to be the most frequently detected gene in patients with epilepsy.^11,12,25 However, in another Chinese study including 733 children with epilepsy onset within the first year of life, KCNQ2 was the second most common pathogenic gene following SCN1A.¹³ When we analyzed the variant spectrum within our cohort of patients younger than 1 year, PRRT2 surpassed SCN1A to become the most common pathogenic gene. These results suggest, on one hand, that the genetic spectrum of epilepsy in the Chinese population might differ from that in other populations, with PRRT2 potentially being more common; on the other hand, it indicates that interpreting genetic testing results needs consideration of the inclusion criteria of study population such as onset age.

In our cohort, diagnostic variants were most frequently located on chromosomes 2 and 16, which was similar to previous findings.^11,12 This could be attributed to the essential roles played by SCN1A and PRRT2 genes, located on chromosomes 2 and 16, respectively. Furthermore, chromosome 16 is known to be gene-rich, which might increase its susceptibility to genetic diseases.^26,27 We also found that chromosome 16 had the highest frequency of detected CNVs. These specific chromosomes, which are more predisposed to genomic imbalances and rearrangements due to their enrichment in duplicated segments, have also been reported as predominant carriers of pathogenic CNVs in studies of Western populations.²⁸

There were 12 patients in our cohort with dual or triple genetic diagnoses. For example, a 1-year-10-month-old boy (patient 8 in eTable 2) presented with epilepsy and developmental delay. Both he and his father exhibited café-au-lait macules, and his father had cutaneous neurofibromas. Neurofibromatosis type 1 was initially suspected before genetic testing, which revealed 2 variants: a paternally inherited 17q11.2 deletion and a de novo STXBP1 variant. Both the 17q11.2 deletion and STXBP1 variant could explain the patient's phenotypes.^29-32 As proposed previously,¹¹ a conventional assay designed to detect either SNVs or CNVs independently might miss the identification of such combined genetic variants. This underscores the importance of considering the possibility of a second pathogenic variant in patients exhibiting clinical symptoms highly suggestive of a genetic disease but simultaneously presenting with atypical manifestations.

In previous studies, limited data of mitochondrial gene variants were available. In the cohort, mitochondrial sequencing was performed in 165 patients, identifying 3 mitochondrial variants in 6 patients. Notably, 4 of them carried MT-TL1(m.3243A > G), a hotspot variant associated with mitochondrial epilepsy.^33,34 This variant can result in various manifestations, such as mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes, and its outcomes can range from asymptomatic to lethal, possibly due to varying variant loads.³⁵ The 4 patients with the m.3243A > G variant in the cohort also exhibited other abnormal symptoms including hyperlactatemia, muscle weakness, headache, vomiting, and short stature. Given that all 4 patients were children, long-term follow-up is necessary because additional phenotypes may develop over time. Another frequently reported variant, MT-TK (m.8344A > G),^33,34,36,37 was not found in our cohort.

This study has several limitations. Some patients who underwent gene panel testing were excluded because of diagnostic considerations based on clinical manifestations, such as some neurocutaneous syndromes. This exclusion may limit the generalizability of our findings in capturing the full genetic landscape of pediatric epilepsy. In addition, variants currently classified as VUS may be reclassified as more evidence becomes available. Future research could aim to include a broader patient cohort and use more comprehensive genetic testing methods.

This study reinforces the importance of integrating genetic testing into the clinical management of epilepsy, demonstrating that incorporating WES into patient care can yield valuable insights into diagnosis, treatment, and genetic research.

Glossary

CAE

childhood absence epilepsy

CNV

copy number variant

DS

Dravet syndrome

EE/DEE-SWAS

epileptic encephalopathy/developmental epileptic encephalopathy with spike-and-wave activation in sleep

EIDEE

early-infantile developmental and epileptic encephalopathy

EMAtS

epilepsy with myoclonic-atonic seizures

FS+

febrile seizure plus

GEFS+

genetic epilepsy with febrile seizures plus

GO

gene ontology

IESS

infantile epileptic spasms syndrome

LGS

Lennox-Gastaut syndrome

LP

likely pathogenic

P

pathogenic

SeLECTS

self-limited epilepsy with centrotemporal spikes

SNV

single nucleotide variant

VUS

variants of uncertain significance

WES

whole-exome sequencing

Author Contributions

F. Wu: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. X. Ji: drafting/revision of the manuscript for content, including medical writing for content; study concept or design; analysis or interpretation of data. P. Cheng: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. S. Zhao: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. S. Feng: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. W. Liu: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data.J. Chen: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. X. Zhang: drafting/revision of the manuscript for content, including medical writing for content; study concept or design. H. Wang: drafting/revision of the manuscript for content, including medical writing for content; study concept or design. Q. Chen: drafting/revision of the manuscript for content, including medical writing for content; study concept or design.

Study Funding

This work was supported by several funds throughout the conduct of this study: the National Key Research and Development Program of China [grant number 2022YFC2703903], Capital's Funds for Health Improvement and Research [grant number 2022-2Z-2104], and Wu Jieping Medical Foundation Program [grant number 320.6750.2021-04-33].

Disclosure

The authors report no relevant disclosures. Go to Neurology.org/NG for full disclosures.