Identification of potential susceptibility genes in patients with Vestibular Migraine through whole exome sequencing

Identification of potential susceptibility genes in patients with Vestibular Migraine through whole exome sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of potential susceptibility genes in patients with Vestibular Migraine through whole exome sequencing Xueqing Zhang, Qiaomei Deng, Chao Wen, Xiaobang Huang, Jiarong Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7361519/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study systematically analyzed the exonic regions of patients with vestibular migraine (VM) and healthy controls to identify VM-associated genetic variants and preliminarily map susceptibility genes. The findings aim to provide a theoretical foundation for elucidating the genetic mechanisms of VM and exploring potential therapeutic targets. Methods Whole-exome sequencing (WES) was performed on 59 VM patients (53 females, 6 males; mean age 49.27±12.77 years) recruited from Tianjin First Central Hospital and 280 healthy controls. All VM patients met the diagnostic criteria established by the Bárány Society, with exclusion of other peripheral vestibular disorders and central vertigo diseases. Result Through high-throughput sequencing and bioinformatics analysis, we identified 64 pathogenic/likely pathogenic variants across 56 genes. Notably, several key genes exhibited high mutation frequencies: The von Willebrand factor gene (VWF) showed variants in all patients (100%), providing direct evidence for the neurovascular hypothesis; The chromatin remodeler RSF1 demonstrated a 57.63% variant rate, implicating epigenetic regulation; Variants in nuclear pore protein NUP210L (54.24%) and cyclic nucleotide-gated channel CNGA1 (32.20%) suggested nuclear-cytoplasmic transport defects and ion channel dysfunction, respectively. These findings molecularly explain the co-occurrence of vestibular symptoms and migraines in VM patients. Notably, mutations in DNAH5 and STRC, associated with ciliary function and hearing loss, were detected in 8.47% of patients. Immune-related genes HLA-A (22.03%), HLA-B (10.17%), and metabolic gene UGT1A1 (23.73%) variants offered new genetic insights into VM-autoimmune disease comorbidity. Functional enrichment analysis revealed susceptibility genes significantly associated with clinical phenotypes: Sensorineural hearing loss (16.07%), Visual disturbances (16.07%), Hypertension (14.29%). Conclusion This study identified the key susceptibility gene profile of VM through WES and conducted functional enrichment analysis, indicating that its pathogenesis may involve pathways such as neurovascular regulation, epigenetic modification, and ion channel dysfunction. These findings provide important clues for understanding the molecular mechanism of the disease, with significant theoretical and clinical value. They lay a foundation for elucidating the complex clinical manifestations of VM, exploring the genotypic characteristics of different VM subtypes, and developing precise diagnostic markers and potential therapeutic targets. Vestibular Migraine Whole exome sequencing Susceptibility genes Clinical manifestations Variants Figures Figure 1 Figure 2 Introduction Vestibular Migraine (VM) is a neurological disorder characterized by the co-occurrence of recurrent episodic vertigo and migraine symptoms. In 2012, it was officially included in the third edition of the International Classification of Headache Disorders (ICHD-3) beta version by the International Headache Society (IHS) [ 1 ]. Its clinical manifestations involve vestibular dysfunction lasting from minutes to 72 hours (including spontaneous vertigo, positional vertigo, and visually induced vertigo, among others), often accompanied by migraine-like headaches, photophobia/phonophobia, and other typical migraine features [ 2 , 3 ]. Epidemiological studies indicate that the prevalence of VM in the general population ranges from 4–5.7%, while its incidence is significantly higher among migraine patients, reaching 10.3%. Women are notably more affected than men (female-to-male ratio 1:3–5) [ 4 ], making VM the second most common cause of central vertigo after benign paroxysmal positional vertigo (BPPV). Although the diagnostic criteria for VM have been progressively refined, its pathophysiological mechanisms remain incompletely understood. Currently, the ICHD-3 diagnosis of VM still primarily relies on clinical phenotypes, lacking objective biomarkers, which poses challenges in differentiating it from conditions such as Ménière’s disease and vestibular paroxysmia [ 5 ]. Significant progress has been made in the genetic research of migraine. Genome-wide association studies (GWAS) have identified over 40 common susceptibility loci, involving genes related to neuronal excitability regulation (e.g., TRPM8, PRDM16), synaptic transmission (e.g., CACNA1A, ATP1A2), and vascular function (e.g., NOS3)[ 6 ]. Among these, multiple studies have demonstrated a strong association between TRPM8 and migraine risk, making it a candidate gene for VM[ 7 , 8 ]. Mutations in ion channel genes such as CACNA1A and SCN1A can lead to familial hemiplegic migraine (FHM)[ 9 , 10 ]. However, these studies predominantly focus on known functional genes or pathways, presenting certain limitations. On one hand, candidate gene studies are constrained by prior knowledge and may overlook unknown pathogenic genes. On the other hand, while GWAS enables genome-wide scanning, the high genetic heterogeneity of VM and insufficient sample sizes often result in weak association signals, making it difficult to pinpoint causative genes. Additionally, the clinical phenotypes of VM are highly diverse, and different subtypes may have distinct genetic underpinnings, which traditional research methods struggle to systematically elucidate." Whole Exome Sequencing (WES) technology targets approximately 2% of the protein-coding regions in the human genome, enabling efficient detection of various genetic variants, including single nucleotide variants (SNVs) and insertions/deletions (Indels). This makes it a powerful tool for identifying pathogenic genes in rare and complex diseases [ 11 , 12 ]. In recent years, WES has been successfully applied to uncover disease-causing genes in neurological disorders such as epilepsy [ 13 ] and Alzheimer's disease [ 14 ], demonstrating significant potential in elucidating disease genetic mechanisms. Given this, the present study employs WES in a cohort of VM patients. By systematically analyzing the exonic regions of VM patients and healthy controls, we aim to screen for genetic variants associated with VM pathogenesis, preliminarily map susceptibility genes, and provide a theoretical foundation for further clarifying the genetic mechanisms of VM and exploring potential therapeutic targets. Materials and methods Participants This study enrolled 59 VM patients (53 females, 6 males; mean age 49.27 ± 12.77 years) who visited the Department of Otolaryngology at Tianjin First Central Hospital between January 2023 and November 2024. VM diagnosis was established according to the Bárány Society diagnostic criteria[ 3 ]. Differential diagnoses excluded peripheral vestibular disorders (e.g., Ménière’s disease, BPPV, sudden deafness with vertigo, Hunt’s syndrome, labyrinthitis) and central vestibular pathologies (e.g., head trauma, stroke, or other central balance disorders). All procedures adhered to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants after full explanation of the study’s purpose and potential implications. The study protocol was approved by the Ethics Review Committee of Tianjin First Central Hospital (Approval No.: YC-BY-LC-2023-0020). Genetic testing and WES Following informed consent, peripheral venous blood samples were collected from probands. Genomic DNA was extracted and subjected to PCR amplification. Exonic regions were captured using targeted sequence capture probes (MyGenostics GenCap), followed by sequencing on the BGI DNBSEQ-T7 platform. Raw sequencing data were collected for subsequent analysis. Variants Selected After sequencing, the raw data were filtered by cutadaptor software. The clean reads were mapped to the UCSC Human Genome 19 (hg19) human reference genome using the parameter BWA of Sentieon software. The duplicated reads were removed using the parameter driver of Sentieon software, and the parameter driver is used to correct the base, so that the quality value of the base in the reads of the final output BAM file can be closer to the real probability of mismatch with the reference genome, and the mapped reads were used for the detection of variation. The variants of SNP and InDel were detected by the parameter driver of Sentieon software. Variants were further annotated by ANNOVAR software, and associated with multiple databases, such as, 1000 genome, ESP6500, dbSNP, EXAC, Inhouse (MyGenostics), HGMD, and also predicted by SIFT, PolyPhen-2, MutationTaster, GERP++. In this study, four steps were used to select the potential pathogenic mutations in downstream analysis: (i) Mutation reads should be more than 5, and mutation ration should be no less than 30%; (ii) The mutations should be removed, when the frequency of mutation was more than 5% in 1000g, ESP6500, and Inhouse database; (iii) The mutations should be dropped, if they were in InNormal database (MyGenostics); (iV) The synonymous mutations should be removed, when they were not in the HGMD database. After that, the rest mutations should be the potential pathogenic mutations for further analysis. Bioinformation analysis Gene Ontology (GO) enrichment analyses were performed based on Matescape database to predict the clinical phenotypes. The online software and human genome databases, including 1000 Genomes Project Phase 3 (Han Chinese in Beijing China), Mutation Taster and ACMG, were applied to identify mutation frequencies and predict the functional effects of the variants. Pathogenicity rating The pathogenicity rating of the variation was determined by conducting a literature search and querying databases. Pathogenicity rating refers to the classification standards and guidelines set by the ACMG/AMP guidelines (American College of Medical Genetics and Genomics/Association for Molecular Pathology), which categorize variants into five tiers: pathogenic (P), likely pathogenic (LP), benign (B), likely benign (LB), and variants of uncertain significance (VUS). For this study, only variants classified as pathogenic (P) or likely pathogenic (LP) were selected for further analysis. Statistical analysis Categorical variables were presented as number (percentage).Comparisons of categorical data were made by chi-square test. p < 0.05 was considered as statistically significant. All statistical analyses were performed using SPSS V25.0 for statistics and GraphPad Prism 10 for visualization. Results 1. General demographic characteristics of subjects In this study, a total of 59 VM patients and 280 healthy controls were enrolled (Table 1 ). Among the 59 confirmed VM cases, the duration of episodes ranged from 2 months to 40 years, with attack frequencies varying from 4 times per year to 5 times per month. The distribution of headache symptoms in the cohort was as follows: one sided location(96.61%), pulsating quality (74.58%), moderate or severe pain intensity (100%), and aggravation by routine physical activity (84.75%). Photophobia/phonophobia was reported in 77.97% of cases, visual aura in 55.93%, motion sickness in 74.58%, and cochlear symptoms (e.g., tinnitus, aural fullness) in 66.10%. Comorbid conditions such as diabetes and hypertension were present in 6.78% and 18.64% of patients, respectively. The clinical characteristics of the VM cohort are detailed in Table 2 . Table 1 Demographic information for individuals with WES data Cohort Number Mean age (SE) Female Male Total Female Male Total VM case 53 6 59 49.6 ± 12.7 46.2 ± 13.4 49.3 ± 12.8 Control 130 150 280 34.9 ± 4.0 34.8 ± 3.6 34.8 ± 3.7 Table 2 Clinical feature of subjects Variables Result Number 59 Age (yr) 49.3 ± 12.8 Sex Male n (%) 6 (10.17) Female n (%) 53 (89.83) Headache characteristics one sided location n (%) 57 (96.61) pulsating quality n (%) 44 (74.58) moderate or severe pain intensity n (%) 59 (100) aggravation by routine physical activity n (%) 50 (84.75) Photophobia and phonophobia 46 (77.97) Visual aura 33 (55.93) Motion sickness 44(74.58%) Cochlear symptoms 39 (66.10) Accompanying diseases Diabetes mellitus n (%) 4 (6.78) Hypertension n (%) 11 (18.64) 2 . Identification of variants from whole exome sequencing A total of 64 pathogenic variant loci of 56 genes were identified by WES (Fig. 1 ). In 59 VM patients, each patient carries 1 to 12 candidate pathologic variants. Variations of the following genes were identified in more than 3 patients: VWF (100.00%, n = 59), RSF1 (1) (57.63%, n = 34), NUP210L (1) (54.24%, n = 32), RSF1 (2) (30.51%, n = 18), UGT1A1 (23.73%, n = 14), HLA-A (22.03%, n = 13), SEPTIN5 (20.34%, n = 12), NUP210L (2) (16.95%, n = 10), RSF1 (3) (16.95%, n = 10), CNCGA1 (32.20%, n = 19), JAKMIP2 (13.56%, n = 8), GGT1 (11.86%, n = 7), HLA-B (10.17%, n = 6), STRC (8.47%, n = 5), TBC1D1 (8.47%, n = 5), DNAH5 (8.47%, n = 5), INPPL1 (6.78%, n = 4), PHOX2A (6.78%, n = 4), BPTF (6.78%, n = 4), LONP1 (6.78%, n = 4), DNAH5 (6.78%, n = 4), MAP3K5 (6.78%, n = 4), GGT1 (6.78%, n = 4), ARID5B (5.08%, n = 3), ERBB2 (5.08%, n = 3), PRDM5 (5.08%, n = 3), PRRC2A (5.08%, n = 3). The molecular information for the pathogenic 28 susceptibility variant loci from 24 genes associated with VM are presented in Table 3 (only variants with mutation counts ≥ 3 in the VM cohort are listed). A total of 64 susceptibility variant loci from 56 genes were confirmed in additional file (Supplementary Table 1). Table 3 Molecular information for the pathogenic variants of genes associated with VM. Gene Chr. Ref Transcript Mutation Type Nucleotide Changes Amino Acid Changes Effect Mutation frequency P value OR MutationTaster Pathogenicity (ACMG) Evidence (ACMG) Case (n = 59) Control(n = 280) VWF chr12 NM_000552.5 SNV c.3438T > G p.Tyr1146Ter stopgain 100.00% (n = 59) 0% (n = 0) 1.55043E-67 66759 Disease causing Likely pathogenic PVS1,PM2 RSF1 chr11 NM_016578.3 SNV c.3217G > T p.Glu1073Ter stopgain 57.63% (n = 34) 1.07% (n = 3) 2.81948E-27 107.2689076 Disease causing Likely pathogenic PVS1,PM2 NUP210L chr1 NM_207308.2 SNV c.1612G > T p.Glu538Ter stopgain 54.24% (n = 32) 1.79% (n = 5) 1.73497E-23 59.19834711 Disease causing Likely pathogenic PVS1,PM2 RSF1 chr11 NM_016578.3 SNV c.3208G > T p.Glu1070Ter stopgain 30.51% (n = 18) 0% (n = 0) 1.87703E-15 250.0843373 Disease causing Likely pathogenic PVS1,PM2 UGT1A1 chr2 NM_000463.3 insertion c.-41_-40dup / unknown 23.73% (n = 14) 0% (n = 0) 5.74878E-12 178.7802198 - Likely pathogenic PS3,PM3 HLA-A chr6 NM_002116.8 indel c.311_315delinsT p.Thr104IlefsTer17 frameshift 22.03% (n = 13) 0% (n = 0) 4.07413E-11 162.8709677 - Likely pathogenic PVS1,PM2 SEPTIN5 chr22 NM_002688.6 SNV c.815-1G > C / splicing 20.34% (n = 12) 0% (n = 0) 2.83456E-10 147.6315789 Disease causing Likely pathogenic PVS1,PM2 NUP210L chr1 NM_207308.2 SNV c.1620 + 1G > T / splicing 16.95% (n = 10) 0% (n = 0) 1.30052E-08 119 Disease causing Likely pathogenic PVS1,PM2 RSF1 chr11 NM_016578.3 SNV c.3205G > T p.Glu1069Ter stopgain 16.95% (n = 10) 0% (n = 0) 1.30052E-08 119 Disease causing Likely pathogenic PVS1,PM2 CNGA1 chr4 NM_001379270.1 SNV c.349G > T p.Glu117Ter stopgain 32.20% (n = 19) 5.00% (n = 14%) 3.23716E-08 8.849297573 Disease causing Likely pathogenic PVS1,PM2 JAKMIP2 chr5 NM_001270941.2 SNV c.1579G > T p.Glu527Ter stopgain 13.56% (n = 8) 0% (n = 0) 5.57082E-07 92.59223301 Disease causing Likely pathogenic PVS1,PM2 GGT1 chr22 NM_001288833.2 deletion c.1261del p.Met421TrpfsTer75 frameshift 11.86% (n = 7) 0% (n = 0) 3.55676E-06 80.14285714 - Likely pathogenic PVS1,PM2 HLA-B chr6 NM_005514.8 indel c.204_209delinsGAGGC p.Glu69ArgfsTer8 frameshift 10.17% (n = 6) 0% (n = 0) 2.23472E-05 68.1588785 - Likely pathogenic PVS1,PM2 STRC chr15 NM_153700.2 deletion c.2303_2313 + 1del / splicing 8.47% (n = 5) 0% (n = 0) 0.000138221 56.6146789 - Likely pathogenic PM2,PM3,PP3 TBC1D1 chr4 NM_015173.4 insertion c.637dup p.Arg213ProfsTer35 frameshift 8.47% (n = 5) 0% (n = 0) 0.000138221 56.6146789 - Likely pathogenic PVS1,PM2 DNAH5 chr5 NM_001369.3 SNV c.5122G > T p.Glu1708Ter stopgain 8.47% (n = 5) 0% (n = 0) 0.000138221 56.6146789 Disease causing Likely pathogenic PVS1,PM2 INPPL1 chr11 NM_001567.4 SNV c.2327-1G > C / splicing 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Pathogenic PVS1,PM3,PM2 PHOX2A chr11 NM_005169.4 SNV c.*5223C > G / unknown 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Likely pathogenic PM2,PM3,PP3 BPTF chr17 NM_182641.4 SNV c.3223G > T p.Glu1075Ter stopgain 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Likely pathogenic PVS1,PM2 LONP1 chr19 NM_004793.4 SNV c.637A > T p.Arg213Ter stopgain 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Likely pathogenic PVS1,PM2,BP4 DNAH5 chr5 NM_001369.3 SNV c.5115-2A > T / splicing 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Likely pathogenic PVS1,PM2 MAP3K5 chr6 NM_005923.4 SNV c.2085C > A p.Tyr695Ter stopgain 6.78% (n = 4) 0% (n = 0) 0.000841894 45.48648649 Disease causing Likely pathogenic PVS1,PM2 ARID5B chr10 NM_032199.3 SNV c.1000G > T p.Glu334Ter stopgain 5.08% (n = 3) 0% (n = 0) 0.005051363 34.75221239 Disease causing Likely pathogenic PVS1,PM2 ERBB2 chr17 NM_004448.4 SNV c.2711A > T p.Asp904Val nonsynonymous 5.08% (n = 3) 0% (n = 0) 0.005051363 34.75221239 Disease causing Likely pathogenic PM2,PP2,PP3 PRDM5 chr4 NM_018699.4 SNV c.1031-1G > T / splicing 5.08% (n = 3) 0% (n = 0) 0.005051363 34.75221239 Disease causing Likely pathogenic PVS1,PM2 PRRC2A chr6 NM_004638.4 SNV c.4621A > T p.Arg1541Ter stopgain 5.08% (n = 3) 0% (n = 0) 0.005051363 34.75221239 Disease causing Likely pathogenic PVS1,PM2 GGT1 chr22 NM_001288833.2 SNV c.295 + 1G > A / splicing 6.78% (n = 4) 0.71% (n = 2) 0.009534566 9.032432432 Disease causing Likely pathogenic PVS1,PM2 3. Function enrichment of susceptibility genes The pathway analysis yielded 20 GO terms with a p-value < 0.01 (Fig. 2 ). The GO terms with the greatest number of genes were “conductive hearing loss” (14.29%, 8 out of 56 genes), “visual impairment” (16.07%, 9 out of 56 genes), “elongated superior cerebellar peduncle” (5.40%, 3 out of 56 genes), “mental retardation, psychosocial” (10.71%, 6 out of 56 genes), “mental deficiency” (10.71%, 6 out of 56 genes), “profound mental retardation” (10.71%, 6 out of 56 genes), “triglycerides measurement” (16.07%, 9 out of 56 genes), “Congenital Epicanthus” (14.29%, 8 out of 56 genes), “Essential Hypertension” (14.29%, 8 out of 56 genes), “Otosclerosis” (7.14%, 4 out of 56 genes), “Abnormal vision” (8.93%, 5 out of 56 genes), “Pseudogout” (5.36%, 3 out of 56 genes), “Complete hydatidiform mole” (7.14%, 4 out of 56 genes), “Tremor” (14.29%, 8 out of 56 genes), “Anteverted nostril” (12.50%, 7 out of 56 genes),“mixed gliomas” (7.14%, 4 out of 56 genes), “Sensorineural Hearing Loss (disorder)” (16.07%, 9 out of 56 genes), “Blepharoptosis” (14.29%, 8 out of 56 genes), “Joubert syndrome 1” (5.36%, 3 out of 56 genes), “Ptosis” (14.29%, 8 out of 56 genes). Discussion This is the first WES study aiming to find genetic variants associated with VM. In the present study,we identified 64 pathogenic/likely pathogenic variant sites across 56 genes among 59 VM patients, with high-frequency variants observed in genes such as VWF (100%), RSF1 (57.63%), NUP210L (54.24%), and CNGA1 (32.20%). These findings not only reveal the complex mechanism of multi-pathway synergy in VM pathogenesis but also provide new molecular insights into understanding the clinical heterogeneity of VM. I. Pathophysiological Significance of Key Genes 1. Neurovascular Regulation Pathway The ubiquitous variation in the VWF gene (100%) provides the most direct genetic evidence to date supporting the neurovascular hypothesis [ 15 ]. As the gene encoding von Willebrand factor (vWF), VWF regulates platelet adhesion and vascular endothelial function, thereby contributing to microcirculatory homeostasis[ 16 ]. A preponderance of available evidence links migraine, and especially aura, to increased levels of von Willebrand factor (vWF) antigen[ 17 ]. The underlying mechanism may involve microcirculatory dysfunction in the inner ear—given that the labyrinthine arterioles are terminal arteries highly sensitive to blood flow changes, VWF variants could induce vasospasms or a prothrombotic state, thereby triggering vertigo attacks[ 18 , 19 ]. Notably, 55.93% of patients in this cohort experienced visual symptoms, which may be closely linked to VWF-mediated retinal microvascular dysfunction. 2. Epigenetic Regulatory Mechanisms As a chromatin remodeling factor, the high-frequency mutation (57.63%) of RSF1 suggests an important role of epigenetic regulation in VM. Studies have shown that RSF1 regulates the dynamics of H2A histone modifications at mitotic centromeres and contributes to the maintenance of chromosome stability[ 20 ]. In addition, posttranslational histone modifications play important roles in regulating chromatin-based nuclear processes. Histone H2AK119 ubiquitination (H2Aub) is a prevalent modification and has been primarily linked to gene silencing. RSF1 acts as an H2Aub reader, contributing to H2Aub-mediated gene silencing by maintaining a stable nucleosome pattern at promoter regions[ 21 ]. Furthermore, RSF1 is involved in inflammatory processes by regulating inflammatory factors such as NF-κB[ 22 ], which may explain the immune-related comorbidities observed in 22.03% of patients in this cohort. 3. Nuclear-Cytoplasmic Transport and Ion Channel Disorders NUP210L (54.24%), as an important component of the nuclear pore complex, encodes a newly validated nucleoporin that contributes to nuclear pore assembly and gene regulation in neuronal progenitors[ 23 ]. Its variations may lead to impairments in the nuclear-cytoplasmic transport of ion channel proteins. Recent studies have found that CNGA1 is highly expressed in the trigeminal ganglion and plays a role in trigeminovascular pain signaling leading to migraine headache by activating the cyclic nucleotide-gated ion channels (CNG) pathway[ 24 ]. Notably, the genes CNGA1 and CNGB1 encode the alpha and beta subunits of the rod CNG channel, a ligand-gated cation channel whose activity is controlled by cyclic guanosine monophosphate (cGMP)[ 25 ]. Autosomal inherited mutations in either of the genes lead to a progressive rod-cone retinopathy known as retinitis pigmentosa (RP). The rod CNG channel is expressed in the plasma membrane of the outer segment and functions as a molecular switch that converts light-mediated changes in cGMP into a voltage and Ca2 + signal[ 26 ]. CNGA1 and CNGB1 form the rod photoreceptor cGMP-gated channel, and their dysfunction disrupts cGMP-Ca²⁺signaling cascades, providing a molecular basis for the co-occurrence of visual and vestibular symptoms in VM. II. Clinical Implications of Genotype-Phenotype Correlations Mutations in DNAH5 (dynein axonemal heavy chain 5)and STRC (stereocilin), associated with ciliary function and hearing loss, were detected in 8.47% of patients. DNAH5 (dynein axonemal heavy chain 5) encodes an axonemal heavy chain dynein, which is part of a microtubule-associated motor protein complex consisting of heavy, light, and intermediate chains[ 27 ]. Mutations in this gene cause primary ciliary dyskinesia type 3, as well as Kartagener syndrome, which are both diseases due to ciliary defects[ 28 ]. STRC (stereocilin) encodes a protein that is associated with the hair bundle of the sensory hair cells in the inner ear[ 29 ]. The hair bundle is composed of stiff microvilli called stereocilia and is involved with mechanoreception of sound waves[ 30 ].Defects in this may impair mechanotransduction in vestibular hair cells. Given that 66.10% of patients also had sensorineural hearing loss, we propose "vestibular ciliopathy" as a potential VM subtype. The recurrence of HLA-A (22.03%), HLA-B (10.17%), and UGT1A1 (23.73%) variants suggests immune/metabolic contributions. HLA genes modulate neuroinflammation [ 31 ], while UGT1A1 (UDP-glucuronosyltransferase) influences detoxification pathways [ 32 , 33 ], potentially affecting trigeminovascular activation.These findings further highlight the possible involvement of immunological mechanisms in the pathogenesis of migraine, and provide a genetic basis for the comorbidity between VM and autoimmune diseases (e.g., Sjögren’s syndrome)[ 34 , 35 ]. Pathway analysis linked susceptibility genes to diverse phenotypes (Fig. 2 ), including sensorineural hearing loss (16.07%), visual impairment (16.07%), and essential hypertension (14.29%). This pleiotropy suggests: Shared biological pathways (e.g., vascular regulation, neuronal excitability) between VM and these comorbidities. Potential endophenotypes (e.g., cochlear symptoms in 66.10% of patients) that may refine VM subtyping. However, terms like "mental retardation" or "congenital epicanthus" likely reflect the broad roles of these genes in development rather than direct VM causality. III. Research Innovations and Limitations As the first whole-exome sequencing study on VM, this research fills the gap in understanding the genetic characteristics of this field. Compared with previous genetic studies on migraine, we identified a significantly distinct gene profile: traditional migraine-related genes (e.g., CACNA1A, ATP1A2) had a mutation frequency of less than 5% in our cohort, whereas the detection rates of new genes such as VWF and RSF1 were much higher than expected. This discrepancy may reflect the unique pathophysiological nature of VM—not merely a subtype of migraine, but an independent disease entity integrating neurovascular dysfunction, ion channel abnormalities, and immune disorders. This study has the following limitations: First, the sample size is constrained by the strict diagnostic criteria for VM. Second, some variants (e.g., HLA) require validation of their population frequency in larger cohorts. Finally, the lack of animal models has led to incomplete mechanistic verification. Future research should expand the sample size for multicenter validation, with particular attention to the genetic patterns of familial VM. Additionally, functional experiments are needed to clarify the pathogenic mechanisms. Conclusion This study preliminarily identified the key susceptibility gene profile of VM through WES and performed functional enrichment analysis. The results suggest that VM pathogenesis may involve multiple pathways, including neurovascular regulation, epigenetic modifications, and ion channel dysfunction, providing critical insights into the molecular mechanisms of this disorder. These findings hold significant theoretical and clinical value: first, they offer a novel genetic perspective for deciphering the complex clinical manifestations of VM; second, they establish a research foundation for future investigations into the genotypic characteristics of different VM subtypes; most importantly, they provide crucial evidence for developing precision diagnostic biomarkers, exploring potential therapeutic targets, and formulating personalized prevention and treatment strategies. Future research should focus on the following directions: expanding sample sizes to validate current findings, and conducting functional experiments to elucidate the precise mechanisms of candidate genes. Ultimately advancing VM diagnosis and treatment toward the era of precision medicine. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by the Tianjin Key Medical Discipline Construction Project, Tianjin Health Research Project (No.TJWJ2022QN027), and incubation fund of Tianjin First Central Hospital(No. 2025FYQN03). Author Contribution WW and TC performed the study design. XZ acquired and analyzed the data. XZ and TC drafted the manuscript. QD, CW, XH, JL and JY revised the manuscript. All authors read and approved the final manuscript. References The International Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia, (2013) 33(9): p. 629–808 Cho SJ et al (2016) Vestibular migraine in multicenter neurology clinics according to the appendix criteria in the third beta edition of the International Classification of Headache Disorders. Cephalalgia 36(5):454–462 Lempert T et al (2012) Vestibular migraine: diagnostic criteria. J Vestib Res 22(4):167–172 Neuhauser HK et al (2005) Epidemiology of vestibular vertigo: a neurotologic survey of the general population. Neurology 65(6):898–904 Pajaniappane A (2024) Assessment and management of vestibular migraine within ENT. J Laryngol Otol 138(S2):S22–S26 Gormley P et al (2016) Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 48(8):856–866 Oh EH et al (2020) TRPM7 as a Candidate Gene for Vestibular Migraine. Front Neurol 11:595042 Dussor G, Cao YQ (2016) TRPM8 and Migraine. Headache 56(9):1406–1417 de Boer I, Hansen JM, Terwindt GM (2024) Hemiplegic migraine. Handb Clin Neurol 199:353–365 Villar-Martinez MD, Moreno-Ajona D, Goadsby PJ (2024) Familial hemiplegic migraine. Handb Clin Neurol 203:135–144 Tilemis FN et al (2023) Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases. Genes (Basel), 14(7) Petrovski S et al (2019) Whole-exome sequencing in the evaluation of fetal structural anomalies: a prospective cohort study. Lancet 393(10173):758–767 Yacoub AM et al (2025) Whole exome sequencing revealed ultra-rare genetic variations in juvenile myoclonic epilepsy. Neurol Sci 46(2):899–910 Zhang YR et al (2024) Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia. Alzheimers Dement 20(10):7062–7078 Moskowitz MA (2007) Pathophysiology of headache–past and present. Headache 47(1):S58–63 Schneider MF et al (2020) Platelet adhesion and aggregate formation controlled by immobilised and soluble VWF. BMC Mol Cell Biol 21(1):64 Tietjen GE, Collins SA (2018) Hypercoagulability Migraine Headache 58(1):173–183 Yilmaz Avci A et al (2019) Migraine and subclinical atherosclerosis: endothelial dysfunction biomarkers and carotid intima-media thickness: a case-control study. Neurol Sci 40(4):703–711 Levy D, Moskowitz MA (2023) Meningeal Mechanisms and the Migraine Connection. Annu Rev Neurosci 46:39–58 Lee HS et al (2018) The chromatin remodeler RSF1 controls centromeric histone modifications to coordinate chromosome segregation. Nat Commun 9(1):3848 Zhang Z et al (2017) Role of remodeling and spacing factor 1 in histone H2A ubiquitination-mediated gene silencing. Proc Natl Acad Sci U S A 114(38):E7949–E7958 Yang YI et al (2014) RSF1 is a positive regulator of NF-kappaB-induced gene expression required for ovarian cancer chemoresistance. Cancer Res 74(8):2258–2269 Orniacki C et al (2023) Y-complex nucleoporins independently contribute to nuclear pore assembly and gene regulation in neuronal progenitors. J Cell Sci, 136(11) Kruse LS et al (2006) Phosphodiesterase 3 and 5 and cyclic nucleotide-gated ion channel expression in rat trigeminovascular system. Neurosci Lett 404(1–2):202–207 Liu Y et al (2021) Retinal degeneration in mice lacking the cyclic nucleotide-gated channel subunit CNGA1. FASEB J 35(9):e21859 Gerhardt MJ, Petersen-Jones SM, Michalakis S (2023) CNG channel-related retinitis pigmentosa. Vis Res 208:108232 Dong M et al (2025) Genetic spectrum and genotype-phenotype correlations in DNAH5-mutated primary ciliary dyskinesia: a systematic review. Orphanet J Rare Dis 20(1):97 Barber AT et al (2025) The Association of Neonatal Respiratory Distress With Ciliary Ultrastructure and Genotype in Primary Ciliary Dyskinesia. Pediatr Pulmonol 60(5):e71091 Benoit C et al (2023) Behavioral characterization of the cochlear amplifier lesion due to loss of function of stereocilin (STRC) in human subjects. Hear Res 439:108898 Verpy E et al (2001) Mutations in a new gene encoding a protein of the hair bundle cause non-syndromic deafness at the DFNB16 locus. Nat Genet 29(3):345–349 Armangue T et al (2023) Neurologic complications in herpes simplex encephalitis: clinical, immunological and genetic studies. Brain 146(10):4306–4319 Nardone-White DT et al (2021) Detoxication versus Bioactivation Pathways of Lapatinib In Vitro: UGT1A1 Catalyzes the Hepatic Glucuronidation of Debenzylated Lapatinib. Drug Metab Dispos 49(3):233–244 Tu DZ et al (2024) Human UDP-glucuronosyltransferase 1As catalyze aristolochic acid D O-glucuronidation to form a lesser nephrotoxic glucuronide. J Ethnopharmacol 328:118116 Cavestro C, Ferrero M (2018) Migraine in Systemic Autoimmune Diseases. Endocr Metab Immune Disord Drug Targets 18(2):124–134 Ha WS, Chu MK (2024) Altered immunity in migraine: a comprehensive scoping review. J Headache Pain 25(1):95 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1SupplementaryTable1.2025.8.19Sub..xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7361519","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502796060,"identity":"a4e43338-001c-4161-9913-ec2239fc91d0","order_by":0,"name":"Xueqing Zhang","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xueqing","middleName":"","lastName":"Zhang","suffix":""},{"id":502796061,"identity":"6f0459e0-814a-4932-b0a9-437c85711bed","order_by":1,"name":"Qiaomei Deng","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiaomei","middleName":"","lastName":"Deng","suffix":""},{"id":502796064,"identity":"32e6335b-60b1-494a-a8a1-745e62274920","order_by":2,"name":"Chao Wen","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wen","suffix":""},{"id":502796065,"identity":"64e76bb2-3dfe-41b6-bdca-9dfabde248be","order_by":3,"name":"Xiaobang Huang","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaobang","middleName":"","lastName":"Huang","suffix":""},{"id":502796068,"identity":"cc425027-b059-4c66-8929-5b6dc59bbcc2","order_by":4,"name":"Jiarong Li","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Li","suffix":""},{"id":502796071,"identity":"0612cc40-2492-41a5-918c-07ba2c28fe20","order_by":5,"name":"Jianlin Yang","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Yang","suffix":""},{"id":502796074,"identity":"5ccf233f-758c-4b87-95c3-450acd54be67","order_by":6,"name":"Taisheng Chen","email":"","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Taisheng","middleName":"","lastName":"Chen","suffix":""},{"id":502796077,"identity":"c710c47b-c129-451e-84ce-0f038da72123","order_by":7,"name":"Wei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJCCAwkGEnL8EmC2hAxxWh4U2BhLzmBgbABq4SFKC+ODD2mJBjfAWhgIazG4kXwQ6LDDCca3m48/ulFjwcPAfvjoBvxa0hJAWvLM7hxLbM45BnQYT1raDXxazG7nGIC0FJvdyDFszmEDapHgMSOgJf8DSEvi5hkgLf+I0pIDCuS0xA0SQC25bURosb//DOQwG2OJG2mJs3P7JHjYCPlFsufw448//gCjckbygc853+rk+NkPH8OrBROwkaZ8FIyCUTAKRgE2AADVB09aACG/XQAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin First Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-13 06:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7361519/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7361519/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89593181,"identity":"4c44eca1-b14d-4cb2-b4ef-d03f3308c535","added_by":"auto","created_at":"2025-08-21 16:17:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":330201,"visible":true,"origin":"","legend":"\u003cp\u003eSusceptibility genes variant of vestibular migraine identified by whole exome sequencing. \u003cstrong\u003eGene annotation: 1‑10:\u003c/strong\u003e VWF, RSF1, NUP210L,RSF1, UGT1A1, HLA-A, SEPTIN5, NUP210L, RSF1, CNGA1; \u003cstrong\u003e11-20:\u003c/strong\u003e JAKMIP2, GGT1, HLA-B, STRC, TBC1D1, DNAH5, INPPL1, PHOX2A, BPTF, LONP1; \u003cstrong\u003e21-30: \u003c/strong\u003eDNAH5, MAP3K5, ARID5B, ERBB2, PRDM5, PRRC2A, GGT1, IGSF3, FMO3, NPR1; \u003cstrong\u003e31-40:\u003c/strong\u003e PARK7, KIAA0586, ZFYVE26, SLC12A3, CNKSR1, FCN3, SAMD11, AMPD3, ARRB1, CEP295; \u003cstrong\u003e41-50:\u003c/strong\u003e OTOGL, TIMM9, SECISBP2L, CNOT1, MIEF2, NF1, MEOX1, BPTF, CARD14, CIC; \u003cstrong\u003e51-60: \u003c/strong\u003eTNNT1, UNC80, UNC80, CFAP91, TSEN2, NKTR, F12, AHI1, MAP3K5, LHFPL5; \u003cstrong\u003e61-64:\u003c/strong\u003e CD36, RP1L1, SLC26A7, INPP5E\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7361519/v1/4df031c3dcdd82313995aa79.jpeg"},{"id":89591945,"identity":"fa749849-bec8-4317-a9ba-05324c5970da","added_by":"auto","created_at":"2025-08-21 16:09:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":485562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunction enrichment of susceptibility genes of vestibular migraine.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7361519/v1/6a487f8fddfd6fd450b9c86a.jpeg"},{"id":90068724,"identity":"4e3a09f4-da6f-4125-9051-f8e77feca5dc","added_by":"auto","created_at":"2025-08-28 06:17:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1781478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7361519/v1/1023f0ec-0858-4964-bf38-eec17811a837.pdf"},{"id":89591936,"identity":"32a61245-30c2-49a3-a4c8-bc3126ab6bf8","added_by":"auto","created_at":"2025-08-21 16:09:20","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19727,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1SupplementaryTable1.2025.8.19Sub..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7361519/v1/cb3228df1c8227629d3ab7df.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of potential susceptibility genes in patients with Vestibular Migraine through whole exome sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVestibular Migraine (VM) is a neurological disorder characterized by the co-occurrence of recurrent episodic vertigo and migraine symptoms. In 2012, it was officially included in the third edition of the International Classification of Headache Disorders (ICHD-3) beta version by the International Headache Society (IHS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its clinical manifestations involve vestibular dysfunction lasting from minutes to 72 hours (including spontaneous vertigo, positional vertigo, and visually induced vertigo, among others), often accompanied by migraine-like headaches, photophobia/phonophobia, and other typical migraine features [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological studies indicate that the prevalence of VM in the general population ranges from 4\u0026ndash;5.7%, while its incidence is significantly higher among migraine patients, reaching 10.3%. Women are notably more affected than men (female-to-male ratio 1:3\u0026ndash;5) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], making VM the second most common cause of central vertigo after benign paroxysmal positional vertigo (BPPV). Although the diagnostic criteria for VM have been progressively refined, its pathophysiological mechanisms remain incompletely understood. Currently, the ICHD-3 diagnosis of VM still primarily relies on clinical phenotypes, lacking objective biomarkers, which poses challenges in differentiating it from conditions such as M\u0026eacute;ni\u0026egrave;re\u0026rsquo;s disease and vestibular paroxysmia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSignificant progress has been made in the genetic research of migraine. Genome-wide association studies (GWAS) have identified over 40 common susceptibility loci, involving genes related to neuronal excitability regulation (e.g., TRPM8, PRDM16), synaptic transmission (e.g., CACNA1A, ATP1A2), and vascular function (e.g., NOS3)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these, multiple studies have demonstrated a strong association between TRPM8 and migraine risk, making it a candidate gene for VM[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Mutations in ion channel genes such as CACNA1A and SCN1A can lead to familial hemiplegic migraine (FHM)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, these studies predominantly focus on known functional genes or pathways, presenting certain limitations. On one hand, candidate gene studies are constrained by prior knowledge and may overlook unknown pathogenic genes. On the other hand, while GWAS enables genome-wide scanning, the high genetic heterogeneity of VM and insufficient sample sizes often result in weak association signals, making it difficult to pinpoint causative genes. Additionally, the clinical phenotypes of VM are highly diverse, and different subtypes may have distinct genetic underpinnings, which traditional research methods struggle to systematically elucidate.\"\u003c/p\u003e\u003cp\u003eWhole Exome Sequencing (WES) technology targets approximately 2% of the protein-coding regions in the human genome, enabling efficient detection of various genetic variants, including single nucleotide variants (SNVs) and insertions/deletions (Indels). This makes it a powerful tool for identifying pathogenic genes in rare and complex diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In recent years, WES has been successfully applied to uncover disease-causing genes in neurological disorders such as epilepsy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and Alzheimer's disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], demonstrating significant potential in elucidating disease genetic mechanisms. Given this, the present study employs WES in a cohort of VM patients. By systematically analyzing the exonic regions of VM patients and healthy controls, we aim to screen for genetic variants associated with VM pathogenesis, preliminarily map susceptibility genes, and provide a theoretical foundation for further clarifying the genetic mechanisms of VM and exploring potential therapeutic targets.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThis study enrolled 59 VM patients (53 females, 6 males; mean age 49.27\u0026thinsp;\u0026plusmn;\u0026thinsp;12.77 years) who visited the Department of Otolaryngology at Tianjin First Central Hospital between January 2023 and November 2024. VM diagnosis was established according to the B\u0026aacute;r\u0026aacute;ny Society diagnostic criteria[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Differential diagnoses excluded peripheral vestibular disorders (e.g., M\u0026eacute;ni\u0026egrave;re\u0026rsquo;s disease, BPPV, sudden deafness with vertigo, Hunt\u0026rsquo;s syndrome, labyrinthitis) and central vestibular pathologies (e.g., head trauma, stroke, or other central balance disorders). All procedures adhered to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants after full explanation of the study\u0026rsquo;s purpose and potential implications. The study protocol was approved by the Ethics Review Committee of Tianjin First Central Hospital (Approval No.: YC-BY-LC-2023-0020).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenetic testing and WES\u003c/h3\u003e\n\u003cp\u003e Following informed consent, peripheral venous blood samples were collected from probands. Genomic DNA was extracted and subjected to PCR amplification. Exonic regions were captured using targeted sequence capture probes (MyGenostics GenCap), followed by sequencing on the BGI DNBSEQ-T7 platform. Raw sequencing data were collected for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eVariants Selected\u003c/h3\u003e\n\u003cp\u003eAfter sequencing, the raw data were filtered by cutadaptor software. The clean reads were mapped to the UCSC Human Genome 19 (hg19) human reference genome using the parameter BWA of Sentieon software. The duplicated reads were removed using the parameter driver of Sentieon software, and the parameter driver is used to correct the base, so that the quality value of the base in the reads of the final output BAM file can be closer to the real probability of mismatch with the reference genome, and the mapped reads were used for the detection of variation. The variants of SNP and InDel were detected by the parameter driver of Sentieon software. Variants were further annotated by ANNOVAR software, and associated with multiple databases, such as, 1000 genome, ESP6500, dbSNP, EXAC, Inhouse (MyGenostics), HGMD, and also predicted by SIFT, PolyPhen-2, MutationTaster, GERP++.\u003c/p\u003e\u003cp\u003eIn this study, four steps were used to select the potential pathogenic mutations in downstream analysis: (i) Mutation reads should be more than 5, and mutation ration should be no less than 30%; (ii) The mutations should be removed, when the frequency of mutation was more than 5% in 1000g, ESP6500, and Inhouse database; (iii) The mutations should be dropped, if they were in InNormal database (MyGenostics); (iV) The synonymous mutations should be removed, when they were not in the HGMD database. After that, the rest mutations should be the potential pathogenic mutations for further analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformation analysis\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analyses were performed based on Matescape database to predict the clinical phenotypes. The online software and human genome databases, including 1000 Genomes Project Phase 3 (Han Chinese in Beijing China), Mutation Taster and ACMG, were applied to identify mutation frequencies and predict the functional effects of the variants.\u003c/p\u003e\n\u003ch3\u003ePathogenicity rating\u003c/h3\u003e\n\u003cp\u003eThe pathogenicity rating of the variation was determined by conducting a literature search and querying databases. Pathogenicity rating refers to the classification standards and guidelines set by the ACMG/AMP guidelines (American College of Medical Genetics and Genomics/Association for Molecular Pathology), which categorize variants into five tiers: pathogenic (P), likely pathogenic (LP), benign (B), likely benign (LB), and variants of uncertain significance (VUS). For this study, only variants classified as pathogenic (P) or likely pathogenic (LP) were selected for further analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were presented as number (percentage).Comparisons of categorical data were made by chi-square test. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant. All statistical analyses were performed using SPSS V25.0 for statistics and GraphPad Prism 10 for visualization.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. General demographic characteristics of subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 59 VM patients and 280 healthy controls were enrolled (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 59 confirmed VM cases, the duration of episodes ranged from 2 months to 40 years, with attack frequencies varying from 4 times per year to 5 times per month. The distribution of headache symptoms in the cohort was as follows: one sided location(96.61%), pulsating quality (74.58%), moderate or severe pain intensity (100%), and aggravation by routine physical activity (84.75%). Photophobia/phonophobia was reported in 77.97% of cases, visual aura in 55.93%, motion sickness in 74.58%, and cochlear symptoms (e.g., tinnitus, aural fullness) in 66.10%. Comorbid conditions such as diabetes and hypertension were present in 6.78% and 18.64% of patients, respectively. The clinical characteristics of the VM cohort are detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic information for individuals with WES data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMean age (SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVM case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical feature of subjects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (10.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (89.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeadache characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eone sided location n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (96.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epulsating quality n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (74.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate or severe pain intensity n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eaggravation by routine physical activity n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (84.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhotophobia and phonophobia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (77.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisual aura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (55.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMotion sickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(74.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCochlear symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (66.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccompanying diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIdentification of variants from whole exome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eA total of 64 pathogenic variant loci of 56 genes were identified by WES (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 59 VM patients, each patient carries 1 to 12 candidate pathologic variants. Variations of the following genes were identified in more than 3 patients: VWF (100.00%, n\u0026thinsp;=\u0026thinsp;59), RSF1\u003csup\u003e(1)\u003c/sup\u003e (57.63%, n\u0026thinsp;=\u0026thinsp;34), NUP210L\u003csup\u003e(1)\u003c/sup\u003e (54.24%, n\u0026thinsp;=\u0026thinsp;32), RSF1\u003csup\u003e(2)\u003c/sup\u003e (30.51%, n\u0026thinsp;=\u0026thinsp;18), UGT1A1 (23.73%, n\u0026thinsp;=\u0026thinsp;14), HLA-A (22.03%, n\u0026thinsp;=\u0026thinsp;13), SEPTIN5 (20.34%, n\u0026thinsp;=\u0026thinsp;12), NUP210L\u003csup\u003e(2)\u003c/sup\u003e (16.95%, n\u0026thinsp;=\u0026thinsp;10), RSF1\u003csup\u003e(3)\u003c/sup\u003e (16.95%, n\u0026thinsp;=\u0026thinsp;10), CNCGA1 (32.20%, n\u0026thinsp;=\u0026thinsp;19), JAKMIP2 (13.56%, n\u0026thinsp;=\u0026thinsp;8), GGT1 (11.86%, n\u0026thinsp;=\u0026thinsp;7), HLA-B (10.17%, n\u0026thinsp;=\u0026thinsp;6), STRC (8.47%, n\u0026thinsp;=\u0026thinsp;5), TBC1D1 (8.47%, n\u0026thinsp;=\u0026thinsp;5), DNAH5 (8.47%, n\u0026thinsp;=\u0026thinsp;5), INPPL1 (6.78%, n\u0026thinsp;=\u0026thinsp;4), PHOX2A (6.78%, n\u0026thinsp;=\u0026thinsp;4), BPTF (6.78%, n\u0026thinsp;=\u0026thinsp;4), LONP1 (6.78%, n\u0026thinsp;=\u0026thinsp;4), DNAH5 (6.78%, n\u0026thinsp;=\u0026thinsp;4), MAP3K5 (6.78%, n\u0026thinsp;=\u0026thinsp;4), GGT1 (6.78%, n\u0026thinsp;=\u0026thinsp;4), ARID5B (5.08%, n\u0026thinsp;=\u0026thinsp;3), ERBB2 (5.08%, n\u0026thinsp;=\u0026thinsp;3), PRDM5 (5.08%, n\u0026thinsp;=\u0026thinsp;3), PRRC2A (5.08%, n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\n\u003cp\u003eThe molecular information for the pathogenic 28 susceptibility variant loci from 24 genes associated with VM are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (only variants with mutation counts\u0026thinsp;\u0026ge;\u0026thinsp;3 in the VM cohort are listed). A total of 64 susceptibility variant loci from 56 genes were confirmed in additional file (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMolecular information for the pathogenic variants of genes associated with VM.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eChr.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRef Transcript\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMutation Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNucleotide Changes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAmino Acid Changes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMutation frequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMutationTaster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePathogenicity (ACMG)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEvidence (ACMG)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl(n\u0026thinsp;=\u0026thinsp;280)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_000552.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.3438T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Tyr1146Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00% (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55043E-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_016578.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.3217G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu1073Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.63% (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.81948E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.2689076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNUP210L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_207308.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1612G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu538Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.24% (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73497E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.19834711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_016578.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.3208G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu1070Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.51% (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87703E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.0843373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUGT1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_000463.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einsertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.-41_-40dup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.73% (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74878E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.7802198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePS3,PM3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_002116.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eindel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.311_315delinsT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Thr104IlefsTer17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.03% (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.07413E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.8709677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSEPTIN5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_002688.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.815-1G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.34% (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83456E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.6315789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNUP210L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_207308.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1620\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.95% (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30052E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_016578.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.3205G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu1069Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.95% (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30052E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNGA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001379270.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.349G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu117Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.20% (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00% (n\u0026thinsp;=\u0026thinsp;14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23716E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.849297573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAKMIP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001270941.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1579G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu527Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.56% (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.57082E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.59223301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001288833.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edeletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1261del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Met421TrpfsTer75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.86% (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.55676E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.14285714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_005514.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eindel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.204_209delinsGAGGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu69ArgfsTer8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.17% (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23472E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.1588785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_153700.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edeletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.2303_2313\u0026thinsp;+\u0026thinsp;1del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000138221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.6146789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM2,PM3,PP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBC1D1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_015173.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einsertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.637dup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg213ProfsTer35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000138221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.6146789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDNAH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001369.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.5122G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu1708Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000138221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.6146789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINPPL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001567.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.2327-1G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM3,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHOX2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_005169.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.*5223C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM2,PM3,PP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBPTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_182641.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.3223G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu1075Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLONP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_004793.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.637A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg213Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2,BP4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDNAH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001369.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.5115-2A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP3K5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_005923.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.2085C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Tyr695Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48648649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARID5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_032199.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1000G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Glu334Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005051363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.75221239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERBB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_004448.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.2711A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Asp904Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enonsynonymous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005051363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.75221239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM2,PP2,PP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRDM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_018699.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.1031-1G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005051363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.75221239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRRC2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_004638.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.4621A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg1541Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005051363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.75221239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNM_001288833.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec.295\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78% (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71% (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009534566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.032432432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease causing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLikely pathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePVS1,PM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3. Function enrichment of susceptibility genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pathway analysis yielded 20 GO terms with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The GO terms with the greatest number of genes were \u0026ldquo;conductive hearing loss\u0026rdquo; (14.29%, 8 out of 56 genes), \u0026ldquo;visual impairment\u0026rdquo; (16.07%, 9 out of 56 genes), \u0026ldquo;elongated superior cerebellar peduncle\u0026rdquo; (5.40%, 3 out of 56 genes), \u0026ldquo;mental retardation, psychosocial\u0026rdquo; (10.71%, 6 out of 56 genes), \u0026ldquo;mental deficiency\u0026rdquo; (10.71%, 6 out of 56 genes), \u0026ldquo;profound mental retardation\u0026rdquo; (10.71%, 6 out of 56 genes), \u0026ldquo;triglycerides measurement\u0026rdquo; (16.07%, 9 out of 56 genes), \u0026ldquo;Congenital Epicanthus\u0026rdquo; (14.29%, 8 out of 56 genes), \u0026ldquo;Essential Hypertension\u0026rdquo; (14.29%, 8 out of 56 genes), \u0026ldquo;Otosclerosis\u0026rdquo; (7.14%, 4 out of 56 genes), \u0026ldquo;Abnormal vision\u0026rdquo; (8.93%, 5 out of 56 genes), \u0026ldquo;Pseudogout\u0026rdquo; (5.36%, 3 out of 56 genes), \u0026ldquo;Complete hydatidiform mole\u0026rdquo; (7.14%, 4 out of 56 genes), \u0026ldquo;Tremor\u0026rdquo; (14.29%, 8 out of 56 genes), \u0026ldquo;Anteverted nostril\u0026rdquo; (12.50%, 7 out of 56 genes),\u0026ldquo;mixed gliomas\u0026rdquo; (7.14%, 4 out of 56 genes), \u0026ldquo;Sensorineural Hearing Loss (disorder)\u0026rdquo; (16.07%, 9 out of 56 genes), \u0026ldquo;Blepharoptosis\u0026rdquo; (14.29%, 8 out of 56 genes), \u0026ldquo;Joubert syndrome 1\u0026rdquo; (5.36%, 3 out of 56 genes), \u0026ldquo;Ptosis\u0026rdquo; (14.29%, 8 out of 56 genes).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first WES study aiming to find genetic variants associated with VM. In the present study,we identified 64 pathogenic/likely pathogenic variant sites across 56 genes among 59 VM patients, with high-frequency variants observed in genes such as VWF (100%), RSF1 (57.63%), NUP210L (54.24%), and CNGA1 (32.20%). These findings not only reveal the complex mechanism of multi-pathway synergy in VM pathogenesis but also provide new molecular insights into understanding the clinical heterogeneity of VM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI. Pathophysiological Significance of Key Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Neurovascular Regulation Pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ubiquitous variation in the VWF gene (100%) provides the most direct genetic evidence to date supporting the neurovascular hypothesis [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. As the gene encoding von Willebrand factor (vWF), VWF regulates platelet adhesion and vascular endothelial function, thereby contributing to microcirculatory homeostasis[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. A preponderance of available evidence links migraine, and especially aura, to increased levels of von Willebrand factor (vWF) antigen[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The underlying mechanism may involve microcirculatory dysfunction in the inner ear\u0026mdash;given that the labyrinthine arterioles are terminal arteries highly sensitive to blood flow changes, VWF variants could induce vasospasms or a prothrombotic state, thereby triggering vertigo attacks[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, 55.93% of patients in this cohort experienced visual symptoms, which may be closely linked to VWF-mediated retinal microvascular dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Epigenetic Regulatory Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a chromatin remodeling factor, the high-frequency mutation (57.63%) of RSF1 suggests an important role of epigenetic regulation in VM. Studies have shown that RSF1 regulates the dynamics of H2A histone modifications at mitotic centromeres and contributes to the maintenance of chromosome stability[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, posttranslational histone modifications play important roles in regulating chromatin-based nuclear processes. Histone H2AK119 ubiquitination (H2Aub) is a prevalent modification and has been primarily linked to gene silencing. RSF1 acts as an H2Aub reader, contributing to H2Aub-mediated gene silencing by maintaining a stable nucleosome pattern at promoter regions[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, RSF1 is involved in inflammatory processes by regulating inflammatory factors such as NF-\u0026kappa;B[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], which may explain the immune-related comorbidities observed in 22.03% of patients in this cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Nuclear-Cytoplasmic Transport and Ion Channel Disorders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNUP210L (54.24%), as an important component of the nuclear pore complex, encodes a newly validated nucleoporin that contributes to nuclear pore assembly and gene regulation in neuronal progenitors[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Its variations may lead to impairments in the nuclear-cytoplasmic transport of ion channel proteins. Recent studies have found that CNGA1 is highly expressed in the trigeminal ganglion and plays a role in trigeminovascular pain signaling leading to migraine headache by activating the cyclic nucleotide-gated ion channels (CNG) pathway[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, the genes CNGA1 and CNGB1 encode the alpha and beta subunits of the rod CNG channel, a ligand-gated cation channel whose activity is controlled by cyclic guanosine monophosphate (cGMP)[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Autosomal inherited mutations in either of the genes lead to a progressive rod-cone retinopathy known as retinitis pigmentosa (RP). The rod CNG channel is expressed in the plasma membrane of the outer segment and functions as a molecular switch that converts light-mediated changes in cGMP into a voltage and Ca2\u0026thinsp;+\u0026thinsp;signal[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. CNGA1 and CNGB1 form the rod photoreceptor cGMP-gated channel, and their dysfunction disrupts cGMP-Ca\u0026sup2;⁺signaling cascades, providing a molecular basis for the co-occurrence of visual and vestibular symptoms in VM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eII. Clinical Implications of Genotype-Phenotype Correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutations in DNAH5 (dynein axonemal heavy chain 5)and STRC (stereocilin), associated with ciliary function and hearing loss, were detected in 8.47% of patients. DNAH5 (dynein axonemal heavy chain 5) encodes an axonemal heavy chain dynein, which is part of a microtubule-associated motor protein complex consisting of heavy, light, and intermediate chains[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mutations in this gene cause primary ciliary dyskinesia type 3, as well as Kartagener syndrome, which are both diseases due to ciliary defects[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. STRC (stereocilin) encodes a protein that is associated with the hair bundle of the sensory hair cells in the inner ear[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The hair bundle is composed of stiff microvilli called stereocilia and is involved with mechanoreception of sound waves[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].Defects in this may impair mechanotransduction in vestibular hair cells. Given that 66.10% of patients also had sensorineural hearing loss, we propose \"vestibular ciliopathy\" as a potential VM subtype.\u003c/p\u003e\n\u003cp\u003eThe recurrence of HLA-A (22.03%), HLA-B (10.17%), and UGT1A1 (23.73%) variants suggests immune/metabolic contributions. HLA genes modulate neuroinflammation [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], while UGT1A1 (UDP-glucuronosyltransferase) influences detoxification pathways [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], potentially affecting trigeminovascular activation.These findings further highlight the possible involvement of immunological mechanisms in the pathogenesis of migraine, and provide a genetic basis for the comorbidity between VM and autoimmune diseases (e.g., Sj\u0026ouml;gren\u0026rsquo;s syndrome)[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Pathway analysis linked susceptibility genes to diverse phenotypes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), including sensorineural hearing loss (16.07%), visual impairment (16.07%), and essential hypertension (14.29%). This pleiotropy suggests: Shared biological pathways (e.g., vascular regulation, neuronal excitability) between VM and these comorbidities. Potential endophenotypes (e.g., cochlear symptoms in 66.10% of patients) that may refine VM subtyping. However, terms like \"mental retardation\" or \"congenital epicanthus\" likely reflect the broad roles of these genes in development rather than direct VM causality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIII. Research Innovations and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the first whole-exome sequencing study on VM, this research fills the gap in understanding the genetic characteristics of this field. Compared with previous genetic studies on migraine, we identified a significantly distinct gene profile: traditional migraine-related genes (e.g., CACNA1A, ATP1A2) had a mutation frequency of less than 5% in our cohort, whereas the detection rates of new genes such as VWF and RSF1 were much higher than expected. This discrepancy may reflect the unique pathophysiological nature of VM\u0026mdash;not merely a subtype of migraine, but an independent disease entity integrating neurovascular dysfunction, ion channel abnormalities, and immune disorders.\u003c/p\u003e\n\u003cp\u003eThis study has the following limitations: First, the sample size is constrained by the strict diagnostic criteria for VM. Second, some variants (e.g., HLA) require validation of their population frequency in larger cohorts. Finally, the lack of animal models has led to incomplete mechanistic verification. Future research should expand the sample size for multicenter validation, with particular attention to the genetic patterns of familial VM. Additionally, functional experiments are needed to clarify the pathogenic mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study preliminarily identified the key susceptibility gene profile of VM through WES and performed functional enrichment analysis. The results suggest that VM pathogenesis may involve multiple pathways, including neurovascular regulation, epigenetic modifications, and ion channel dysfunction, providing critical insights into the molecular mechanisms of this disorder. These findings hold significant theoretical and clinical value: first, they offer a novel genetic perspective for deciphering the complex clinical manifestations of VM; second, they establish a research foundation for future investigations into the genotypic characteristics of different VM subtypes; most importantly, they provide crucial evidence for developing precision diagnostic biomarkers, exploring potential therapeutic targets, and formulating personalized prevention and treatment strategies. Future research should focus on the following directions: expanding sample sizes to validate current findings, and conducting functional experiments to elucidate the precise mechanisms of candidate genes. Ultimately advancing VM diagnosis and treatment toward the era of precision medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Tianjin Key Medical Discipline Construction Project, Tianjin Health Research Project (No.TJWJ2022QN027), and incubation fund of Tianjin First Central Hospital(No. 2025FYQN03).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWW and TC performed the study design. XZ acquired and analyzed the data. XZ and TC drafted the manuscript. QD, CW, XH, JL and JY revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe International Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia, (2013) 33(9): p. 629\u0026ndash;808\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho SJ et al (2016) Vestibular migraine in multicenter neurology clinics according to the appendix criteria in the third beta edition of the International Classification of Headache Disorders. Cephalalgia 36(5):454\u0026ndash;462\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLempert T et al (2012) Vestibular migraine: diagnostic criteria. J Vestib Res 22(4):167\u0026ndash;172\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeuhauser HK et al (2005) Epidemiology of vestibular vertigo: a neurotologic survey of the general population. Neurology 65(6):898\u0026ndash;904\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePajaniappane A (2024) Assessment and management of vestibular migraine within ENT. J Laryngol Otol 138(S2):S22\u0026ndash;S26\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGormley P et al (2016) Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 48(8):856\u0026ndash;866\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOh EH et al (2020) TRPM7 as a Candidate Gene for Vestibular Migraine. Front Neurol 11:595042\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDussor G, Cao YQ (2016) TRPM8 and Migraine. Headache 56(9):1406\u0026ndash;1417\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Boer I, Hansen JM, Terwindt GM (2024) Hemiplegic migraine. Handb Clin Neurol 199:353\u0026ndash;365\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVillar-Martinez MD, Moreno-Ajona D, Goadsby PJ (2024) Familial hemiplegic migraine. Handb Clin Neurol 203:135\u0026ndash;144\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTilemis FN et al (2023) Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases. Genes (Basel), 14(7)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetrovski S et al (2019) Whole-exome sequencing in the evaluation of fetal structural anomalies: a prospective cohort study. Lancet 393(10173):758\u0026ndash;767\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYacoub AM et al (2025) Whole exome sequencing revealed ultra-rare genetic variations in juvenile myoclonic epilepsy. Neurol Sci 46(2):899\u0026ndash;910\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang YR et al (2024) Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia. Alzheimers Dement 20(10):7062\u0026ndash;7078\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoskowitz MA (2007) Pathophysiology of headache\u0026ndash;past and present. Headache 47(1):S58\u0026ndash;63\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneider MF et al (2020) Platelet adhesion and aggregate formation controlled by immobilised and soluble VWF. BMC Mol Cell Biol 21(1):64\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTietjen GE, Collins SA (2018) Hypercoagulability Migraine Headache 58(1):173\u0026ndash;183\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYilmaz Avci A et al (2019) Migraine and subclinical atherosclerosis: endothelial dysfunction biomarkers and carotid intima-media thickness: a case-control study. Neurol Sci 40(4):703\u0026ndash;711\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevy D, Moskowitz MA (2023) Meningeal Mechanisms and the Migraine Connection. Annu Rev Neurosci 46:39\u0026ndash;58\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HS et al (2018) The chromatin remodeler RSF1 controls centromeric histone modifications to coordinate chromosome segregation. Nat Commun 9(1):3848\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z et al (2017) Role of remodeling and spacing factor 1 in histone H2A ubiquitination-mediated gene silencing. Proc Natl Acad Sci U S A 114(38):E7949\u0026ndash;E7958\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang YI et al (2014) RSF1 is a positive regulator of NF-kappaB-induced gene expression required for ovarian cancer chemoresistance. Cancer Res 74(8):2258\u0026ndash;2269\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrniacki C et al (2023) Y-complex nucleoporins independently contribute to nuclear pore assembly and gene regulation in neuronal progenitors. J Cell Sci, 136(11)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKruse LS et al (2006) Phosphodiesterase 3 and 5 and cyclic nucleotide-gated ion channel expression in rat trigeminovascular system. Neurosci Lett 404(1\u0026ndash;2):202\u0026ndash;207\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y et al (2021) Retinal degeneration in mice lacking the cyclic nucleotide-gated channel subunit CNGA1. FASEB J 35(9):e21859\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerhardt MJ, Petersen-Jones SM, Michalakis S (2023) CNG channel-related retinitis pigmentosa. Vis Res 208:108232\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong M et al (2025) Genetic spectrum and genotype-phenotype correlations in DNAH5-mutated primary ciliary dyskinesia: a systematic review. Orphanet J Rare Dis 20(1):97\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarber AT et al (2025) The Association of Neonatal Respiratory Distress With Ciliary Ultrastructure and Genotype in Primary Ciliary Dyskinesia. Pediatr Pulmonol 60(5):e71091\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenoit C et al (2023) Behavioral characterization of the cochlear amplifier lesion due to loss of function of stereocilin (STRC) in human subjects. Hear Res 439:108898\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerpy E et al (2001) Mutations in a new gene encoding a protein of the hair bundle cause non-syndromic deafness at the DFNB16 locus. Nat Genet 29(3):345\u0026ndash;349\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmangue T et al (2023) Neurologic complications in herpes simplex encephalitis: clinical, immunological and genetic studies. Brain 146(10):4306\u0026ndash;4319\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNardone-White DT et al (2021) Detoxication versus Bioactivation Pathways of Lapatinib In Vitro: UGT1A1 Catalyzes the Hepatic Glucuronidation of Debenzylated Lapatinib. Drug Metab Dispos 49(3):233\u0026ndash;244\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu DZ et al (2024) Human UDP-glucuronosyltransferase 1As catalyze aristolochic acid D O-glucuronidation to form a lesser nephrotoxic glucuronide. J Ethnopharmacol 328:118116\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCavestro C, Ferrero M (2018) Migraine in Systemic Autoimmune Diseases. Endocr Metab Immune Disord Drug Targets 18(2):124\u0026ndash;134\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHa WS, Chu MK (2024) Altered immunity in migraine: a comprehensive scoping review. J Headache Pain 25(1):95\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vestibular Migraine, Whole exome sequencing, Susceptibility genes, Clinical manifestations, Variants","lastPublishedDoi":"10.21203/rs.3.rs-7361519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7361519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study systematically analyzed the exonic regions of patients with vestibular migraine (VM) and healthy controls to identify VM-associated genetic variants and preliminarily map susceptibility genes. The findings aim to provide a theoretical foundation for elucidating the genetic mechanisms of VM and exploring potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole-exome sequencing (WES) was performed on 59 VM patients (53 females, 6 males; mean age 49.27±12.77 years) recruited from Tianjin First Central Hospital and 280 healthy controls. All VM patients met the diagnostic criteria established by the Bárány Society, with exclusion of other peripheral vestibular disorders and central vertigo diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough high-throughput sequencing and bioinformatics analysis, we identified 64 pathogenic/likely pathogenic variants across 56 genes. Notably, several key genes exhibited high mutation frequencies: The von Willebrand factor gene (VWF) showed variants in all patients (100%), providing direct evidence for the neurovascular hypothesis; The chromatin remodeler RSF1 demonstrated a 57.63% variant rate, implicating epigenetic regulation; Variants in nuclear pore protein NUP210L (54.24%) and cyclic nucleotide-gated channel CNGA1 (32.20%) suggested nuclear-cytoplasmic transport defects and ion channel dysfunction, respectively. These findings molecularly explain the co-occurrence of vestibular symptoms and migraines in VM patients. Notably, mutations in DNAH5 and STRC, associated with ciliary function and hearing loss, were detected in 8.47% of patients. Immune-related genes HLA-A (22.03%), HLA-B (10.17%), and metabolic gene UGT1A1 (23.73%) variants offered new genetic insights into VM-autoimmune disease comorbidity. Functional enrichment analysis revealed susceptibility genes significantly associated with clinical phenotypes: Sensorineural hearing loss (16.07%), Visual disturbances (16.07%), Hypertension (14.29%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified the key susceptibility gene profile of VM through WES and conducted functional enrichment analysis, indicating that its pathogenesis may involve pathways such as neurovascular regulation, epigenetic modification, and ion channel dysfunction. These findings provide important clues for understanding the molecular mechanism of the disease, with significant theoretical and clinical value. They lay a foundation for elucidating the complex clinical manifestations of VM, exploring the genotypic characteristics of different VM subtypes, and developing precise diagnostic markers and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of potential susceptibility genes in patients with Vestibular Migraine through whole exome sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 16:09:10","doi":"10.21203/rs.3.rs-7361519/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47c2f561-f40a-445f-a851-3d60c47efc5d","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T06:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 16:09:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7361519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7361519","identity":"rs-7361519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}