Molecular Characterization and Genotype-phenotype Correlations of SYNGAP1 Variants in a Polish Pediatric Cohort With Neurodevelopmental Disorders

Molecular Characterization and Genotype-phenotype Correlations of SYNGAP1 Variants in a Polish Pediatric Cohort With Neurodevelopmental Disorders | 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 Molecular Characterization and Genotype-phenotype Correlations of SYNGAP1 Variants in a Polish Pediatric Cohort With Neurodevelopmental Disorders ALEKSANDRA JEZELA-STANEK, TOMASZ SKALSKI, KAROLINA CHWIAŁKOWSKA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7635804/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 Background Pathogenic variants in SYNGAP1 are a major cause of developmental and epileptic encephalopathy, typically presenting with intellectual disability, epilepsy, and autism spectrum disorder. Despite increasing recognition worldwide, genotype–phenotype data from Central and Eastern Europe remain limited. Methods We conducted a nationwide study of 30 unrelated Polish pediatric patients carrying pathogenic or likely pathogenic SYNGAP1 variants. Variant classification followed ACMG/ClinGen guidelines. Clinical phenotyping used a structured numerical scoring system. Genotype–phenotype relationships were explored with non-metric multidimensional scaling and PERMANOVA. Results We identified 30 pathogenic or likely pathogenic variants, including truncating, splice-site, and missense substitutions; 50% were novel. Haploinsufficiency emerged as the main pathogenic mechanism, though some missense variants suggested additional effects. Phenotypic analysis showed marked heterogeneity, but severe global developmental delay, intellectual disability, and epilepsy were consistently observed. Epilepsy affected > 80% of patients and frequently required polytherapy. Genotype–phenotype clustering demonstrated broader symptom variability in missense carriers compared with truncating or splice-site variants. Conclusion This first national cohort study from Poland broadens the SYNGAP1 mutational spectrum and highlights the consistently severe neurodevelopmental phenotype, particularly epilepsy and intellectual disability. Our results refine genotype–phenotype correlations, emphasize the clinical impact of haploinsufficiency, and provide a framework for patient stratification in future trials and emerging therapeutic approaches. SYNGAP1 gene developmental epileptic encephalopathy genotype-phenotype correlation haploinsufficiency intellectual disability autism spectrum disorder Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Comprehensive study of variants in 30 Polish pediatric patients. • 50% of identified variants in cohort are novel pathogenic changes. • Truncating variants linked to severe developmental delay and epilepsy. • Missense variants show broader symptom variability and milder cognitive impact. • High epilepsy prevalence with frequent polytherapy treatment in severe cases. • NMDS and PERMANOVA analyses reveal distinct genotype-phenotype clusters. • Study confirms haploinsufficiency as the main pathogenic mechanism. • Emphasizes need for personalized medicine and targeted therapeutic strategies. • Future work: larger cohorts, international collaborations, and advanced therapies. INTRODUCTION The SYNGAP1 gene, located on chromosome 6p21.3, encodes the synaptic Ras GTPase-activating protein SynGAP, a critical regulator of synaptic signaling and plasticity (Gamache et al., 2023; Clement et al., 2012). SynGAP is predominantly localized in the postsynaptic density of excitatory neurons, where it modulates multiple intracellular signaling pathways essential for synaptic development and function (Araki et al., 2020; Ozkan et al., 2014). Its principal role involves acting as a Ras GTPase-activating protein, catalyzing the conversion of active Ras-GTP to inactive Ras-GDP, thereby tightly regulating the Ras/ERK (extracellular signal-regulated kinase) signaling cascade (Guo et al., 2021; Komiyama et al., 2002). This pathway is fundamental for activity-dependent synaptic plasticity mechanisms, such as long-term potentiation (LTP), which underlie learning and memory processes (Ivenshitz and Segal, 2010; Rumbaugh et al., 2006). Pathogenic variants most commonly result in haploinsufficiency, which manifests as intellectual disability, epilepsy, and autism spectrum disorder (Vlaskamp et al., 2019; Mignot et al., 2016). This diminution disrupts the fine balance of synaptic signaling, leading to excessive Ras/ERK pathway activation and subsequent impairment of synaptic maturation and plasticity (Kilinc et al., 2018; Aceti et al., 2015). Clinically, this manifests as a spectrum of neurodevelopmental disorders characterized by global developmental delay, intellectual disability, epilepsy, and autism spectrum disorder (Berryer et al., 2013; Mignot et al., 2016; Hamdan et al., 2009). More than two-thirds of patients with SYNGAP1 -related disorders harbor truncating variants, which are strongly associated with severe neurodevelopmental phenotypes, while a smaller subset carry missense or splicing variants with variable clinical severity (Hamdan et al., 2011; Ozkan et al., 2015). Despite the absence of curative treatments currently, ongoing research is exploring targeted therapeutic strategies, including gene therapy aimed at restoring SYNGAP1 expression, and pharmacological modulation of downstream pathways such as the Ras/ERK cascade (Penn et al., 2017; Kilinc et al., 2021). Early behavioral and cognitive interventions also remain vital in improving functional outcomes for affected individuals (Vlaskamp et al., 2019). This study aims to comprehensively characterize the spectrum of pathogenic and likely pathogenic variants identified in the SYNGAP1 gene within a nationwide cohort of 30 Polish pediatric patients diagnosed with SYNGAP1 -related neurodevelopmental disorders. Specifically, the objectives include detailed molecular analysis of identified genetic variants to define their nature, location, and predicted functional impact using contemporary bioinformatic and clinical classification guidelines. Concurrently, the study seeks to systematically collect and quantitatively assess detailed clinical and neurological phenotypes—including developmental, epileptic, and behavioral features—using a structured symptom scoring system. By integrating genetic and phenotypic data, the study endeavors to investigate genotype-phenotype correlations and elucidate patterns of symptom manifestations associated with different variation types (e.g., truncating, missense, splice-site). These insights refine our understanding of SYNGAP1 -related disorders and provide a framework for patient stratification in upcoming clinical trials and targeted therapeutic approaches. Ultimately, the research supports the development of stratified and personalized clinical management strategies and informs ongoing translational therapeutic efforts such as gene therapy and targeted pharmacological interventions for this rare but impactful disorder. MATERIALS AND METHODS Patient Cohort Characteristics This study included a Polish nationwide cohort of 30 children diagnosed with Intellectual Developmental Disorder, Autosomal Dominant 5 (MRD5), caused by pathogenic or likely pathogenic variants in the SYNGAP1 gene (Vlaskamp et al., 2019; Mignot et al., 2016). All participants were of Polish origin and were evaluated in specialized pediatric neurology or medical genetics departments across Poland, consistent with standards applied in comparable international neurogenetic cohorts (Berryer et al., 2013; Hamdan et al., 2011). Genetic testing was performed as part of routine diagnostic services in accredited nationwide genetics laboratories following national clinical and regulatory frameworks (Hamdan et al., 2009). Informed consent was obtained from the patients or their parents/legal guardians in compliance with national regulations and the Declaration of Helsinki (World Medical Association, 2013). Clinical and genetic data were meticulously collected through structured medical questionnaires and standardized data tables completed by attending physicians or parents, ensuring comprehensive phenotypic documentation (Mignot et al., 2016). Specific cohort characteristics included: Cohort size: 30 children Gender distribution: 15 males and 15 females Age at clinical diagnosis: 2 to 15 years Molecular confirmation: All cases harbored pathogenic or likely pathogenic SYNGAP1 variants consistent with the MRD5 phenotype, Table 1 (Vlaskamp et al., 2019; Hamdan et al., 2011) Inheritance pattern: The majority of variants were de novo , confirmed by parental testing or inferred through clinical and laboratory data (Berryer et al., 2013; Mignot et al., 2016). Inclusion criteria required confirmed molecular diagnosis, availability of detailed clinical records, and parental consent for anonymized data usage in scientific research, aligning with ethical and clinical best practices applied globally in rare neurodevelopmental disorders (Hamdan et al., 2009; Vlaskamp et al., 2019). Symptom assessment and Scoring System (SS) To achieve a structured and comparable characterization of neurological and behavioral symptoms across the cohort, a detailed numerical point-based scoring system was implemented. This system quantified the severity and presence of key clinical features by assigning predefined numerical values to each symptom domain, enabling standardized assessment across patients with variable clinical manifestations (clinical details are in Supplementary Table1a, Table 1b). The scoring framework encompassed a broad range of neurological and developmental features, including microcephaly, torticollis, hypotonia (graded from normal tone to reduced central tone and increased peripheral tone), developmental delay, intellectual disability (ranging from none to severe for children aged six years or older), and developmental regression. Additional parameters captured epilepsy-related characteristics, such as seizure type (categorized from absence to tonic-clonic seizures and seizures exclusively during sleep), EEG abnormalities (scored from normal to generalized or focal atypical discharges), antiepileptic drug use (binary assessment of medication count), and presence of epileptic encephalopathy. Behavioral and imaging features were also systematically recorded, with brain imaging results classified by the presence or absence of structural abnormalities, and autism spectrum disorder (ASD) and related behavioral phenotypes graded along a spectrum from no signs to confirmed autism and behavioral issues. To facilitate meaningful inter-patient comparisons and subsequent multivariate analyses, each patient’s aggregate symptom scores were normalized by dividing the individual domain scores by the respective maximum possible score within that domain, thereby scaling all scores to a common range between 0 and 1. This normalization procedure ensured that symptom severity measurements were directly comparable across diverse clinical domains and allowed for robust statistical analyses of genotype-phenotype correlations and symptom clustering within the cohort. This quantitative approach was further supplemented by qualitative descriptions obtained through parental interviews and clinical observations to fully capture complex neurodevelopmental and behavioral presentations that may elude numerical scoring alone (Tables 2A-1D). Implementation A detailed clinical record was completed for each patient using this comprehensive framework, integrating both quantitative symptom profiles and supporting qualitative descriptions where applicable. This structured approach facilitated robust intra-cohort comparisons, enabling the identification of symptom patterns and severities within the group (Mignot et al., 2016; Vlaskamp et al., 2019). Moreover, the standardized data collection and scoring system allowed for rigorous analysis of genotype–phenotype correlations, providing insights into how distinct SYNGAP1 variants contribute to phenotypic heterogeneity (Hamdan et al., 2011; Berryer et al., 2013). Importantly, this methodology supports extrapolation to future meta-analyses of rare neurodevelopmental disorders by offering a scalable and reproducible assessment model that can harmonize data across international cohorts (Mignot et al., 2016; Hamdan et al., 2009). Where necessary, subjective clinical observations and detailed parent interviews supplemented the numerical scoring to capture complex neurodevelopmental or behavioral presentations that are not easily quantifiable by standardized scales alone, reflecting best practices in phenotypic characterization within rare disease research (Srivastava et al., 2014; Kazdoba et al., 2016). Genetic variant molecular assessment The genetic variants identified in the diagnostic setting and deemed causal for patient symptoms by the attending physician underwent further rigorous evaluation by experienced clinical genomic scientists. Molecular analyses and formal variant classifications were conducted following the most current standards as outlined by the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) guidelines (Richards et al., 2015; Harrison et al., 2023). All SYNGAP1 variants were annotated against the MANE Select transcript NM_006772.3 to ensure consistency and accuracy in variant reporting. Genomic coordinates were mapped using the Genome Reference Consortium Human Reference sequence version 38 (GRCh38/hg38). To assess the potential impact of variants on mRNA splicing, in silico prediction tools SpliceAI and Pangolin were employed, leveraging deep learning-based models to robustly predict splicing alterations (Jaganathan et al., 2019; Zhang et al., 2021). The possible effect of missense variants was further evaluated using the REVEL metapredictor, which integrates multiple tools for improved predictive accuracy of deleterious missense variants (Ioannidis et al., 2016). Population allele frequencies were examined in the Genome Aggregation Database (gnomAD) version 4.1.0 (Karczewski et al., 2020). Variant interpretation was supported by aided by information gathered from comprehensive literature and case report reviews, as well as several clinical variant databases, including ClinVar (NCBI), the Leiden Open Variation Database (LOVD), and the Human Gene Mutation Database (HGMD public). Statistical analysis The statistical analysis aimed to elucidate differences in neurologic symptom profiles among SYNGAP1 variant subgroups through a comprehensive multivariate framework. Non-metric multidimensional scaling (NMDS) was employed to reduce the multidimensional symptom score data into a lower-dimensional space, thereby enabling visualization of complex relationships and patient classification based on symptom similarity (Clarke, 1993). NMDS was performed using the Bray–Curtis dissimilarity metric, which is well-suited for ecological and clinical datasets containing mixed data types and accounts for both presence-absence and abundance information. To rigorously assess the statistical significance of group separations observed in the NMDS ordination, Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted using the adonis function available in the vegan package for R (Anderson, 2001; Oksanen et al., 2020). PERMANOVA tests whether the centroids of predefined variant groups differ significantly in the multidimensional symptom space, with significance evaluated via 999 permutations to robustly control type I error rates. Further, Similarity Percentage Analysis (SIMPER) was utilized to decompose the overall dissimilarity between variant groups and to identify individual symptom variables contributing most substantially to the observed differences (Clarke, 1993). Mean symptom evaluation scores across variant classes were compared to pinpoint neurologic features driving group differentiation, offering insights into specific phenotypic domains affected by different variant types. All analyses were performed using R (version 4.5.1) with the vegan community ecology package, with a standard alpha threshold of p < 0.05 applied to determine statistical significance throughout the study. The co-occurrence patterns of neurologic symptoms within the SYNGAP1 patient cohort were analyzed using Cytoscape version 3.10.3, a widely used open-source platform for complex network visualization and analysis (Shannon et al., 2003). Symptom co-occurrence networks were constructed by quantifying pairwise associations between symptoms based on their simultaneous presence or severity levels across patients. These associations were translated into edges connecting nodes representing individual symptoms, enabling the visualization of symptom interrelationships. RESULTS 1. General clinical characteristics This cohort comprised 30 Polish children diagnosed with SYNGAP1 -related intellectual developmental disorder, autosomal dominant 5 (MRD5), with equal gender distribution (15 males, 15 females). The age at genetic diagnosis ranged from 1.5 to 13 years, with a median age of approximately 4 years. All identified SYNGAP1 variants (30/30, 100%) occurred de novo , confirming the sporadic nature of this condition. Neurological manifestations were nearly universal in our cohort. Hypotonia was present in 28/30 patients (93.3%), typically presenting as reduced muscle tone in the central axis with variable involvement of the extremities. Significant developmental delays were observed in 27/30 patients (90%), while 3 patients exhibited minor delays. Intellectual disability was documented in all patients over 6 years of age, with severity distribution as follows: mild in 3 patients (10%), moderate in 18 patients (60%), and severe in 6 patients (20%). Developmental regression was observed in 12/30 patients (40.0%), often triggered by infections or during periods of therapeutic discontinuity. Epileptic manifestations were prominent features, with seizures occurring in 26/30 patients (86.7%). The most common seizure types included absence seizures and myoclonic seizures, often presenting in combination. EEG abnormalities were documented in 25/30 patients (83.3%), predominantly showing generalized epileptiform changes. Epileptic encephalopathy was diagnosed in 22/30 patients (73.3%), reflecting the severe impact of seizure activity on neurodevelopmental progression. Antiepileptic drug management varied widely, with valproic acid and lamotrigine being the most commonly prescribed medications. Behavioral and psychiatric features were extensively documented, with autism spectrum disorder (ASD) diagnosed in 24/30 patients (80%). Common behavioral abnormalities included stereotypic movements, sensory integration disorders, sleep disturbances, aggressive behaviors, and hypersensitivity to auditory stimuli. Many patients exhibited characteristic autistic behaviors such as hand flapping, fixations on specific objects or activities, and difficulties with social interaction and communication. Physical characteristics showed microcephaly in only 2/30 patients (6.7%), indicating that head circumference is typically within normal limits in this population. Torticollis was rare, present in only 1 patient. Brain MRI findings were predominantly normal (15/30 patients) or showed minor non-specific changes (12/30 patients), with only 3 patients displaying serious malformations. Additional clinical features included various ophthalmologic abnormalities (nystagmus, astigmatism, hyperopia), gastrointestinal issues (feeding difficulties, gastroesophageal reflux, constipation), sleep disorders, and altered pain sensitivity. Several patients demonstrated unusual pain tolerance and hypersensitivity to environmental stimuli, particularly loud sounds and bright lights. The clinical onset typically occurred within the first year of life in most patients, with early signs including hypotonia, feeding difficulties, and delayed developmental milestones. The comprehensive phenotypic spectrum confirms SYNGAP1 haploinsufficiency as a significant cause of neurodevelopmental disability with characteristic epileptic and behavioral manifestations in the Polish pediatric population. 2. SYNGAP1 Variants Analysis In this Polish nationwide cohort, we comprehensively evaluated 30 distinct genetic variants classified as causal for SYNGAP1 -related intellectual developmental disorder (MRD5) (Table 1). Each variant was rigorously classified as pathogenic or likely pathogenic according to ACMG/ClinGen guidelines, ensuring high confidence in their clinical relevance. The identified variants encompassed a heterogeneous array of molecular alterations, including nonsense, frameshift, splice-site, and missense, collectively reflecting the established mutational spectrum of SYNGAP1 -associated MRD5. Detailed variant annotations and patient-specific data are provided in the cohort summary tables (Supplementary Tables 1). 2.1 Variant molecular types Truncating Variants: The majority consisted of truncating variants predicted to introduce premature termination codons, such as nonsense and frameshift changes (e.g., c.155C>A, p.Ser52Ter; c.2993delC, p.Pro978Hisfs99; c.2789del, p.Pro930Leufs147). These variants are anticipated to induce loss-of-function predominantly via mechanisms like nonsense-mediated mRNA decay or generation of severely truncated, non-functional SynGAP proteins. Splice-Site Variants: A subset of patients harbored variants affecting canonical splice donor or acceptor sites or adjacent regulatory regions (e.g., c.664-2A>G, c.388-2A>C), which are predicted to disrupt normal pre-mRNA splicing and consequently diminish functional protein production or alter transcript composition. In silico splice prediction tools support their potential functional consequences. Missense Variants: Pathogenic or likely pathogenic missense changes (e.g., c.1685C>T, p.Pro562Leu; c.1036delG, p.Val346Ter) were less frequent but involve evolutionarily conserved residues within critical functional domains, directly compromising SynGAP protein function through altered enzymatic activity or protein stability. 2.2 Genomic localization Variant type: rhombus, truncating (nonsense and indel frameshifts); squares, splicing; hexagon, missense and in-frame indel. ACMG classification: red, Pathogenic or Likely Pathogenic; navy, VUS (Variant of Unknown Significance). Filled, de novo ; hatched, inherited; contoured, unknown (not tested). Domains and protein motifs are presented according to UniProt (Q96PV0 - SYGP1_HUMAN). The variants predominantly clustered within functional domains essential for SynGAP activity, notably the Ras GTPase-activating (RasGAP) domain and the C2 lipid-binding domain. This distribution spans multiple exons, underscoring the molecular diversity of pathogenic lesions capable of disrupting SYNGAP1 function (Table 1). 2.3 Zygosity and inheritance patterns All pathogenic variants identified were heterozygous, consistent with the autosomal dominant inheritance of MRD5. Parental testing or clinical inference confirmed a predominantly de novo origin of these variants, aligning with global epidemiological data. No evidence for cases of mosaic parental transmission was detected in this cohort. 2.4 Description of novel variants, not reported elsewhere Notably, 15 (50%) of the variants were novel, not previously reported in the literature or public (non-commercial) clinical databases as present in patients with SYNGAP1 -related intellectual developmental disorder: Patient 3: NM_006772.3:c.3350del p.(Gly1117AlafsTer13) - a guanine deletion leading to glycine to alanine change in codon 1117 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 5: NM_006772.3:c.1760_1792del p.(Arg587_Leu598delinsIle) - a deletion of 33 nucleotides resulting in 12 amino acids deletion(from codon 587 to 598) and replacement with isoleucine in Ras-GAP domain, covering important protein residues (PM4; PM1_Supporting). Multiple computational evidence predict deleterious effect on the gene product (FATHMM-indel: pathogenic, MutationTaster for indels: deleterious; PP3). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Likely pathogenic. Patient 6: NM_006772.3:c.3377del p.(Gly1126ValfsTer4) - a single nucleotide deletion leading to glycine to valine change in codon 1126 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic.. Patient 8: NM_006772.3:c.2789del p.(Pro930LeufsTer147) - a single nucleotide deletion leading to proline to leucine change in codon 930 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 9: NM_006772.3:c.3794+1G>A p.? - a canonical splice site variant affecting the donor site of intron 16, predicted to disrupt normal splicing and leading to frameshift (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 11: NM_006772.3:c.3338del p.(Gly1113AlafsTer17) - a single nucleotide deletion leading to glycine to alanine change in codon 1113 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 13: NM_006772.3:c.1253_1256del p.(Lys418SerfsTer21) - a deletion of four nucleotides leading to lysine to serine change in codon 418 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 15: NM_006772.3:c.1036del p.(Val346Ter) - a single nucleotide deletion resulting in premature stop codon at position 346 (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 17: NM_006772.3:c.2232del p.(Gln744HisfsTer16) - a single nucleotide deletion leading to glutamine to histidine change in codon 744 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 21: NM_006772.3:c.1676+5G>A p.? - a non-canonical splice site variant predicted by SpliceAI and Pangolin to affect the donor site of intron 10 and disrupt normal splicing (PP3). Leading to the same splicing effect with similar predictions (<10% difference by Pangolin) as a previously established pathogenic variant (PS1_Supporting). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Likely pathogenic. Patient 22: NM_006772.3:c.155C>A p.(Ser52Ter) - a single nucleotide substitution resulting in premature stop codon at position 52 (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 24: NM_006772.3:c.2933del p.(Pro978HisfsTer99) - a single nucleotide deletion leading to proline to histidine change in codon 978 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic Patient 25: NM_006772.3:c.2362del p.(Ser788ProfsTer21) - a single nucleotide deletion leading to serine to proline change in codon 788 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 29: NM_006772.3:c.439_440del p.(Gln147ThrfsTer4) - a two-nucleotide deletion leading to glutamine to threonine change in codon 147 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. Patient 30: NM_006772.3:c.2115+89_2116-2del p.? - a non-coding indel variant covering the canonical acceptor splice site, predicted to disrupt normal splicing and lead to frameshift (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as de novo in a proband with phenotype associated with SYNGAP1 -related condition (PS2). ACMG classification: Pathogenic. 2.5 SYNGAP1 patient subgroup characteristics by variant type Truncating variants (e.g., c.155C>A p.Ser52Ter, c.2993delC p.Pro978Hisfs99) demonstrate significant phenotypic variability rather than uniform severity. Patient c.155C>A p.Ser52Ter presented with a relatively mild phenotype characterized by absence of seizures, no developmental regression, excluded autism diagnosis, and intellectual disability ranging from moderate to average. In contrast, patient c.2993delC p.Pro978Hisfs99 exhibited a severe phenotype with absence and myoclonic seizures, severe intellectual disability, and confirmed ASD diagnosis. Notably, neither truncating variant patient demonstrated developmental regression before age 3, contradicting previous assumptions about this phenotypic feature. Splice-site variant (c.388-2A>C) showed a complex phenotype with seizure patterns resembling the severe truncating variant, but uniquely displayed developmental regression (absent in both truncating cases), suggesting distinct pathophysiological mechanisms rather than simple functional equivalency to truncating variants. Missense variants (c.1685C>T p.Pro562Leu, c.895C>T p.Arg299Cys) resulted in moderately severe phenotypes with variable seizure burden, moderate intellectual disability, and behavioral abnormalities. Notably, c.895C>T p.Arg299Cys demonstrated significantly preserved cognitive function with mild intellectual disability, representing the best cognitive outcome in the cohort, while c.1685C>T p.Pro562Leu showed moderate intellectual disability with atonic seizures. 3. Non-metric multidimensional scaling data 3.1 Neurological symptoms. The results presented on Fig. 2 indicate substantial variability in neurologic symptom evaluations among the patient cohort, as illustrated by both violin plots and a patient-symptom heatmap. The violin plots reveal that certain symptoms, such as "Normal brain MRI," cluster strongly toward higher (better) evaluation scores, suggesting that the majority of patients have near-normal findings for this characteristic. In contrast, symptoms like "Epileptic encephalopathy," "Intellectual disability," and "Hypotonia" display wider or even multimodal distributions, reflecting a broader spectrum of severity and outcomes among patients. Median evaluation scores for these symptoms are lower, and the spread of the data implies marked inter-individual heterogeneity. The accompanying heatmap underscores these findings by showing the distribution of symptom evaluations across individual patients. For some symptoms, such as "Epileptic encephalopathy" and "Intellectual disability," the heatmap reveals extensive variation, with frequent alternations between low (blue) and high (red) evaluation scores across the patient set. This pattern is indicative of diverse clinical presentations, with some patients experiencing severe impairment while others are relatively less affected. More homogeneous patterns, such as that observed for "Normal brain MRI," confirm the earlier observation from the violin plots. Collectively, these results demonstrate that the burden and manifestations of neurologic symptoms differ greatly within the cohort. Certain neurologic features are relatively spared across most patients, while others show high variability, indicating subgroups within the population with distinct clinical trajectories. This heterogeneity may have implications for prognosis, therapeutic targeting, and individualized care planning. 3.2. Clinical spectrum. The analysis revealed significant relationships between developmental regression (DEV_REG), developmental delayed (DELDEV), and intellectual disability (ID_MI-SEV), which frequently co-occurred, pointing to shared underlying mechanisms likely rooted in disrupted synaptic signaling. Additionally, behavioral manifestations (BEH_MAN) showed a strong association with intellectual disability, further emphasizing the multifaceted and interconnected impact of SYNGAP1 variants on neurodevelopmental and behavioral outcomes. 3.3. Patient ordination and symptom contribution. Variants differentiated into three main types as presented in Vlaskamp et al. (2019): (1) truncating variants, which included nonsense and frameshift variants; (2) splice-site variants; (3) missense and in-frame insertion/deletion variants. Splice-site changes, depending whether they affected canonical acceptor-donor sites or noncanonical with possible predicted missense effect were analyzed combined with truncating as loss-of-function (LOF) or missense as beside also analyzing them as a distinct category due to the variety of their possible effects on protein function. Non-metric multidimensional scaling (NMDS) analysis was performed to explore the variation in symptom score profiles among patients harboring different variant types based on the genetic variants identified in the SYNGAP1 gene. Two distinct grouping strategies were applied to classify patients based on their symptom scores. In the first classification scheme (Figure 4A), patients segregated into three variant categories: truncating, missense, and splicing variants. The NMDS plot revealed partial overlap among groups but demonstrated noticeable separation between missense and splicing variant carriers. This distinction was confirmed by Permutational Multivariate Analysis of Variance (PERMANOVA), which identified a statistically significant difference between missense and splicing groups (p < 0.05). These findings suggest that missense and splicing variants are associated with distinct symptom score profiles, reflecting potential differences in their underlying molecular or clinical phenotypes. In the second grouping strategy (Figure 4B), patients were classified into missense and loss-of-function variant groups. NMDS analysis illustrated clear separation between these two clusters, with minimal overlap. PERMANOVA confirmed that the differences between missense and loss-of-function groups were statistically significant (p < 0.05). This result indicates that loss-of-function and missense variants confer distinct symptomatology patterns within the cohort. Together, these analyses highlight the heterogeneity of symptom manifestations according to variant type. The NMDS coordinates represent underlying multidimensional relationships in patient data, with clustering patterns that point toward variant-specific clinical subtypes. This variant-dependent phenotypic diversity may have important implications for disease prognosis, stratification, and tailored therapeutic approaches. 3.4. Response to antiepileptic treatment. In Figure 5A, patients classified under the second grouping strategy were divided into Split (blue polygon) and Truncate (green polygon) variant groups. The radar plot reveals distinct mean symptom profiles between the two classes. Notably, symptoms such as Developmental regression, Intellectual disability, and EEG abnormalities show varying degrees of severity between the groups, with the Split variant group generally demonstrating higher evaluation scores for several symptoms compared to the Truncate group. This suggests differential clinical impact of these variant types on neurological manifestations. Figure 5B shows a comparison between Missense (orange polygon) and Loss-of-Function (blue polygon) variant groups based on the fourth grouping strategy. The mean symptom evaluation profiles again differ across multiple domains, with Missense variants tending to exhibit relatively elevated scores on some symptom axes, including seizures and behavioral manifestations, relative to Loss-of-Function variants. Conversely, Loss-of-Function variants show higher severity in other symptoms, indicating heterogeneous effects of these variant categories on the neurological phenotype. The analysis of therapeutic approaches in SYNGAP1 -related disorders revealed distinct patterns in the management of epilepsy, reflecting the diverse severity of symptoms and the complexity of treatment strategies. Polytherapy, in line with expectations, was notably prevalent among patients with severe seizures, particularly those characterized by frequent loss of consciousness or epileptic encephalopathy. This finding underscores the clinical challenge in managing epilepsy in SYNGAP1 variant carriers, as these severe cases often exhibit resistance to standard monotherapy. The high prevalence of polytherapy suggests that a single drug is insufficient to control seizures effectively in this subgroup, necessitating a combination of antiepileptic medications to target multiple pathways involved in seizure activity. However, this approach introduces additional challenges, including an increased risk of drug interactions, cumulative side effects, and a potential impact on overall quality of life. Such cases highlight the critical need for precise diagnostic tools and tailored therapeutic strategies that consider the unique genetic and phenotypic profiles of these patients. Conversely, single-drug treatments were observed to be more effective in patients with milder EEG abnormalities, aligning with prior recommendations in the literature. These cases likely represent a less severe disruption of synaptic signaling pathways, allowing for better seizure control with targeted monotherapy. This finding supports the hypothesis that patients with milder variants or partial SynGAP protein functionality may respond well to a focused therapeutic approach. Importantly, this aligns with the growing emphasis on individualized medicine in epilepsy management, where treatment regimens are tailored not only to symptom severity but also to the underlying genetic etiology. The distinction between polytherapy and monotherapy efficacy highlights the need for stratified treatment protocols in SYNGAP1 -related disorders. Patients with severe phenotypes, particularly those exhibiting EEG abnormalities consistent with generalized or multifocal seizure activity, may benefit from early and aggressive intervention, potentially incorporating novel therapeutic approaches such as gene therapy or targeted pharmacological modulation of the Ras/ERK signaling pathway. In contrast, those with milder EEG profiles might achieve optimal outcomes with a conservative, single-drug regimen, reducing the burden of side effects and improving adherence. Additionally, the observed patterns of treatment efficacy emphasize the importance of regular monitoring and re-evaluation of therapeutic strategies in these patients. As new pharmacological options and genetic therapies become available, a dynamic approach to treatment adjustment could further enhance seizure control and quality of life. This analysis underscores the complexity of managing epilepsy in SYNGAP1 -related disorders, highlighting the interplay between genetic, clinical, and therapeutic factors that dictate treatment success. These findings advocate for a multidisciplinary approach, integrating genetic diagnostics, neurologic assessment, and pharmacological expertise to optimize outcomes for this diverse patient population. 4. Correlation with Clinical Scoring System Data are presented in Summary Table: Variant Class and Major Clinical Scores (Table 3) 4.1. Intellectual disability and developmental delay Severity (Scoring: 1–3): Uniformly moderate to severe intellectual disability was observed, regardless of variant class (protein-truncating, splice-site, missense). Scores for most patients clustered at the higher end. Domain effect: Certain reports (including analysis in large international cohorts) propose that variants in exons 1–6 may correlate with milder intellectual disability; however, this pattern was not clearly validated within the scoring profile of this specific Polish cohort [1] [2] . 4.2 Epilepsy Prevalence and morphological type (Scoring: detailed by type): Nearly all patients, independent of variant, scored positively for epilepsy, with high rates of generalized, absence, and myoclonic seizures. Response to therapy: Some literature suggests that variants in exons 4–5 may be associated with more pharmacoresponsive epilepsy, while those in exons 8–15 could be linked to more refractory forms (Li et al., 2023). In this cohort’s scoring matrix, seizure severity and medication counts did not show stratified distribution by variant class. EEG and encephalopathy: Abnormal EEG and epileptic encephalopathy were common across variant types, with no strong inter-group differences in scoring. 4.3 Behavioral and ASD features ASD/Behavioral phenotype (Scoring 0–3): All classes of SYNGAP1 variants led to similar high-scoring profiles for autistic traits, stereotypies, and behavioral disturbances. Domain trend: A potential trend toward reduced ASD risk with variants in the distal part of the gene (exons 1–6) has been proposed in larger datasets, but this effect, if present, was not robust within the present structured scoring results [1] [2] . 4.4 Additional clinical domains Motor and Hypotonia: Scoring for hypotonia, gait disturbance, and microcephaly did not segregate by variant type. MRI Findings: Minor anatomical brain changes were observed in some patients but did not correlate with specific genetic classes. No genotype–scoring stratification emerged for domain or variant class, with minimal effect of variant position on system scoring profiles. 4.5 General Clinical Correlates Consistent with global studies, all children with SYNGAP1 P/LP variants presented with: Global developmental delay and moderate to severe intellectual disability High rates of epilepsy (mainly generalized and myoclonic forms) Frequent autistic traits, abnormal behaviors, and speech impairment Additional findings: microcephaly, gait and coordination problems, and feeding difficulties. No clear genotype–phenotype correlations with specific variant classes were apparent, echoing recent registry-based findings (Wiltrout et al., 2024). The detected spectrum of SYNGAP1 variants in this cohort mirrors the global mutational landscape for MRD5. Most are de novo , protein-disrupting changes with conclusive evidence for pathogenicity, underlying a consistent and severe neurodevelopmental syndrome (Li et al., 2023; Berryer et al., 2013; Mayo Clinic, 2013; Wiltrout et al., 2024; Meili et al., 2021). DISCUSSION This cohort analysis of Polish patients with SYNGAP1 -related neurodevelopmental disorders comprised 30 unrelated individuals (50% females; median age at diagnosis 4.8 years) in whom a spectrum of pathogenic variants was identified. The variant type distribution demonstrated a predominance of loss-of-function variants: truncating variants (nonsense and frameshift) occurred in 18 patients (60.0%), missense variants in 6 patients (20.0%), splice-site variants in 5 patients (16.7%), and in-frame deletions in 1 patient (3.3%). All 30 variants (100%) were confirmed as de novo through parental testing, confirming the sporadic nature of SYNGAP1 -related disorders. The age distribution at diagnosis showed that the majority of cases were recognized in early childhood (2-5 years): 14 patients (46.7%), school age (5-10 years): 9 patients (30.0%), adolescence (>10 years): 5 patients (16.7%), and infancy (0-2 years): 2 patients (6.7%). This cohort reflects the characteristic patterns of SYNGAP1 -related disorders with a predominance of loss-of-function variants (60.0%), significant representation of splicing defects (16.7%), and universal de novo inheritance, which remains consistent with the haploinsufficiency model, where truncating variants were associated with the most severe phenotypes, splice-site variants showed the broadest phenotypic spectrum, and missense variants generally caused milder clinical manifestations. 1. Genotype-phenotype variability Our study identified novel SYNGAP1 variants alongside previously reported variants, providing a comprehensive landscape of the gene's pathogenic profile. Truncating variants, particularly nonsense and frameshift variants, were strongly associated with severe intellectual disability and developmental regression, including developmental and epileptic encephalopathy. These observations are consistent with published reports demonstrating that truncating variants predominantly lead to haploinsufficiency and result in significant phenotypic severity (Mignot et al., 2016; Vlaskamp et al., 2019). The loss-of-function mechanism underlying these variants impairs synaptic signalling pathways critical for neurodevelopment (Hamdan et al., 2009). Interestingly, the milder and more variable symptomatology seen in some patients harbouring missense variants aligns with prior studies suggesting that partial preservation of protein function may mitigate clinical outcomes (Carvill et al., 2013; Meili et al., 2021). However, in our cohort, behavioral phenotypes clustered distinctly in certain missense cases, diverging from many prior reports that primarily emphasized motor and cognitive delays. This divergence implies that additional factors, such as genetic modifiers or environmental influences, may modulate phenotypic expression in SYNGAP1 -related disorders, a hypothesis supported by recent investigations into phenotypic variability in neurodevelopmental conditions (Yang et al. 2021). Pathogenicity in SYNGAP1 -related disorders is principally caused by haploinsufficiency, with 80% of patients in our cohort harboring loss-of-function (LoF) variants (including 60% truncating variants, 16.7% splice-site variants, and 3.3% complex variants). Nevertheless, symptomatic patients with missense variants (20% of our cohort), often classified as variants of uncertain significance (VUS), are increasingly being interpreted as pathogenic when comprehensive functional and clinical evidence accumulates (Jimenez-Gomez et al., 2019). Missense variants in SYNGAP1 represent a mechanistically heterogeneous class, with pathogenic consequences extending beyond simple haploinsufficiency. These effects may include hypermorphic (gain-of-function), neomorphic (novel-function), or dominant-negative impacts, each capable of uniquely disrupting synaptic signaling pathways (Weldon et al., 2018; Mignot et al., 2016). This functional heterogeneity emphasizes the critical importance of robust genotype–phenotype correlations in clinical interpretation and motivates broad, detailed assessments of patient cohorts to capture the full spectrum of SYNGAP1 -related clinical presentations 2. Neurological symptoms and clinical management . The heatmaps and violin plots presented in our results emphasize the high prevalence of seizures and EEG abnormalities across all SYNGAP1 variant types, reinforcing prior research that defines epilepsy as a core clinical feature of SYNGAP1 -related disorders (Mignot et al., 2016). Our findings also highlight a pronounced co-occurrence of developmental regression and epilepsy, indicative of developmental and epileptic encephalopathy, a phenomenon supported by the critical role of SynGAP in synaptic plasticity and neuronal signaling pathways (Vlaskamp et al., 2019; Clement et al., 2012). These shared mechanistic underpinnings suggest that disruptions in SynGAP function perturb synaptic development and excitability, leading to neurodevelopmental deterioration alongside epileptic activity. Compared to previous studies focusing predominantly on Western European or North American populations, our cohort exhibited a somewhat higher prevalence of polytherapy-resistant seizures. This increased seizure treatment refractoriness may reflect regional differences that could stem from genetic background diversity, environmental influences, or disparities in healthcare access and management protocols (Carvill et al., 2013). These findings underscore the need for international collaborative research efforts to elucidate factors underpinning treatment variability and optimize individualized therapeutic strategies for SYNGAP1 -related epilepsy 3. Advanced analytical insights. The non-metric multidimensional scaling (NMDS) analysis conducted in our study provided robust evidence of distinct symptom clustering related to SYNGAP1 variant types. Specifically, patients carrying truncating variants exhibited the most severe clinical phenotype, characterized by profound intellectual disability and frequent epileptic encephalopathy. Conversely, patients with missense variants demonstrated a broader phenotypic spectrum, including cases presenting with milder developmental delays and variable neurological impairment. These results corroborate earlier findings, which similarly delineated genotype-dependent phenotypic variability in SYNGAP1 -related disorders (Vlaskamp et al., 2019; Mignot et al., 2016). Importantly, our study advances the field by applying Similarity Percentage (SIMPER) analysis to quantify the individual symptom contributions driving group differences, highlighting severe intellectual disability, seizures, and developmental regression as key discriminators. The analytical framework employed not only reaffirms well-established genotype-phenotype correlations but also offers a scalable and replicable model for patient stratification in clinical trial design. By stratifying patients into biologically and clinically homogeneous subgroups based on detailed symptom profiles and underlying genetic variants, this approach supports more precise therapeutic targeting and enhances the potential for personalized medicine (Jimenez-Gomez et al., 2019; Holder et al., 2018). As targeted therapies for SYNGAP1 -related disorders, including gene replacement and pathway modulation strategies, continue to develop, such stratification methods will be critical to optimize patient selection and maximize clinical trial efficacy 4. Comparison with literature on therapeutic implications. The therapeutic landscape for SYNGAP1 -related disorders remains in its early stages, with current clinical management predominantly focused on symptomatic treatment. Antiepileptic drugs remain the cornerstone for controlling seizures, complemented by supportive therapies targeting cognitive and behavioral impairments (Vissers et al., 2023). Our study adds to this growing body of evidence by underscoring the considerable variability in treatment response, particularly highlighting that polytherapy is often necessary for patients exhibiting EEG abnormalities and epileptic encephalopathy, reflecting the complexity and severity of seizure phenotypes in SYNGAP1 -related conditions (Jimenez-Gomez et al., 2023; Wiltrout et al., 2024). This variability aligns with prior observations of drug-resistant epilepsy in this population, posing significant clinical challenges (Carvill et al., 2013). Emerging therapeutic strategies hold promises for addressing the underlying molecular pathology of SYNGAP1 disorders. Gene therapy approaches aimed at restoring SYNGAP1 expression or compensating for haploinsufficiency are under active investigation and show encouraging preclinical results (Thomas et al., 2021). Additionally, pharmacological modulation of downstream effectors such as the Ras/ERK signaling pathway represents a promising avenue to rectify synaptic dysfunction directly (Mignot et al., 2016; Clement et al., 2012). Notably, our identification of specific genetic variant clusters within the cohort offers a strategic framework for patient prioritization in these experimental interventions. For instance, carriers of truncating variants who experience severe developmental regression might be ideal candidates for gene replacement therapies, whereas patients harboring missense variants could derive greater benefit from pathway-specific pharmacological modulation (Michaelson et al., 2018; Jimenez-Gomez et al., 2023). This precision medicine approach underlines the critical importance of integrating comprehensive genotype-phenotype data to optimize therapeutic outcomes. 5 . Limitations and recommendations . While our findings align with and extend existing literature, several limitations merit careful consideration. First, the relatively small sample size and regional focus on a Polish pediatric cohort may limit the generalizability of our results to broader, more diverse populations. Expanding future studies to include multiethnic and international cohorts could uncover additional genotype-phenotype correlations and reveal modifiers such as environmental exposures, lifestyle factors, or epigenetic influences that may affect disease expression (Karaca et al., 2018; McRae et al., 2017). Such broader studies are critical to elucidate the heterogeneity and penetrance of SYNGAP1 -related disorders across different populations. Moreover, an important limitation of our study is the reliance on parent-reported clinical data collected through questionnaires, which introduces the potential for subjective bias and recall inaccuracies. This may impact the precision of symptom characterization, particularly for complex behavioral and developmental features (Newman et al., 2016). Future longitudinal research employing comprehensive, standardized clinical assessments, coupled with advanced neuroimaging modalities and molecular biomarker analyses, will provide more objective, granular insights into the natural history and progression of SYNGAP1 -related disorders (Eising et al., 2019; Jensen & Bonde, 2020). Integrating multi-omics approaches and functional studies will also enhance our understanding of the underlying pathophysiological mechanisms and inform tailored therapeutic strategies. 6. Future directions . Our study underscores the critical role of SYNGAP1 in neurodevelopment, emphasizing the gene’s diverse and severe phenotypic consequences spanning intellectual disability, epilepsy, and autism spectrum features. By integrating comprehensive genetic analyses with advanced phenotype quantification and multivariate methods, we provide a robust framework for elucidating the complex interplay between genotype and clinical presentation. This approach not only reveals complex genotype-phenotype correlations, with both truncating and missense variants demonstrating broad phenotypic variability, but also introduces novel insights specific to our Polish cohort, including detailed symptom contribution profiles and nuanced phenotypic heterogeneity that highlights the need for comprehensive functional studies and identification of phenotype-modifying factors beyond variant type alone (Vlaskamp et al., 2019; Jimenez-Gomez et al., 2023). Looking ahead, the expansion of research through international collaborations and larger, more ethnically diverse cohorts will be pivotal in uncovering additional genotype-phenotype relationships and potential disease modifiers, including environmental and epigenetic factors (Karaca et al., 2018; McRae et al., 2017). Furthermore, the integration of cutting-edge therapeutic modalities, such as gene replacement therapies and targeted pharmacological interventions modulating the Ras/ERK pathway, holds significant promise. Such advances will require precise patient stratification informed by comprehensive molecular and clinical profiling to optimize treatment efficacy (Thomas et al., 2021; Mignot et al., 2016). The translational pathway from bench to bedside remains challenging due to the complexity of SYNGAP1 -related disorders, but ongoing progress in genomic medicine and therapeutic development offers renewed hope for more precise and effective interventions in the near future (Clement et al., 2012; Holder et al., 2018). This study highlights the indispensable role of detailed molecular characterization in enhancing diagnostic precision and guiding personalized therapeutic decisions for SYNGAP1 -related conditions. By leveraging genotype-first approaches and sophisticated clinical analytics, our work lays a foundational platform for future research endeavors. These efforts are essential to realize the potential of precision medicine in neurodevelopmental disorders, ultimately improving outcomes for affected individuals worldwide. Conclusion In this well-characterized SYNGAP1 cohort of 30 Polish pediatric patients, our comprehensive scoring system revealed complex genotype-phenotype relationships that challenge simple correlations between variant type and clinical severity. While our analysis confirmed uniformly significant phenotypic impact across all pathogenic variant classes, substantial phenotypic heterogeneity was observed within both truncating and missense variant groups, indicating that factors beyond variant classification influence disease manifestation. Notably, truncating variants demonstrated considerable phenotypic variability rather than uniform severe outcomes. For example, patient 22 with variant c.155C > A p.Ser52Ter presented with a relatively mild phenotype—no seizures, no developmental regression, excluded autism diagnosis, and intellectual disability ranging from moderate to average intelligence. Conversely, patient 24 with c.2993delC p.Pro978Hisfs*99 exhibited severe manifestations including profound intellectual disability and confirmed autism spectrum disorder. This variability within the same variant class suggests that additional genetic modifiers, environmental factors, or epigenetic influences significantly contribute to phenotypic expression. Similarly, missense variants showed broad phenotypic ranges, with some patients presenting severe symptoms comparable to truncating variants, while others demonstrated relatively milder presentations. The absence of statistically significant stratification by variant domain, chromosomal position, or specific variant class underscores the complex pathophysiology underlying SYNGAP1 -related disorders that extends beyond simple haploinsufficiency models. Our findings align with emerging evidence that SYNGAP1 pathogenicity involves multifaceted mechanisms including haploinsufficiency, potential dominant-negative effects, and disruption of critical synaptic plasticity pathways, with clinical severity influenced by multiple genetic and non-genetic factors rather than variant type alone3. This phenotypic complexity necessitates individualized clinical management approaches and highlights the importance of comprehensive functional studies to elucidate the full spectrum of SYNGAP1 pathogenic mechanisms. The uniformly significant developmental and neurological impairment observed across all variant categories, combined with the shared presence of core features such as seizures, hypotonia, and behavioral symptoms, supports SYNGAP1 dysfunction as the primary pathogenic driver, while emphasizing that variant classification alone is insufficient to predict individual patient outcomes or inform precision medicine approaches. Declarations Funding: No funding. Conflict of interest: The authors declare no competing interests. E-mails : Tomasz Skalski: [email protected] Karolina Chwialkowska: [email protected] Author Contribution All authors wrote the main manuscript text, reviewed and accepted the manuscript. References Aceti M, Creson TK, Vaissiere T, Rojas C, Huang WC, Wang YX et al (2015) Syngap1 haploinsufficiency damages a postnatal critical period of pyramidal cell structural maturation linked to cortical circuit assembly. Biol Open 4(6):659–668. https://doi.org/10.1242/bio.20148418 Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26(1):32–46 Araki Y, Zeng M, Zhang M, Huganir RL (2020) Structural basis of synaptic Ras GTPase-activating protein function in learning and memory. Nat Commun 11:4392. https://doi.org/10.1038/s41467-020-18104-5 Berryer MH, Hamdan FF, Klitten LL et al (2013) Mutations in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum Mutat 34(2):385–393. https://doi.org/10.1002/humu.22248 Carvill GL, Regan BM, Yendle SC et al (2013) Mutations in the GTPase-activating protein SynGAP cause intellectual disability and epilepsy. Am J Hum Genet 92(1):89–103. https://doi.org/10.1016/j.ajhg.2012.11.007 Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18(1):117–143 Clement JP, Aceti M, Creson TK, Ozkan ED, Shi Y, Reish NJ et al (2012) Pathogenic SYNGAP1 mutations impair cognitive development by disrupting maturation of dendritic spine synapses. Cell 151(4):709–723. https://doi.org/10.1016/j.cell.2012.08.045 Eising E, Carrion-Castillo A, Vino A et al (2019) A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Neuroimage Clin 23:101842. https://doi.org/10.1016/j.nicl.2019.101842 Gamache TR, Kong E, Kaczorowski CC (2023) Recent advances in understanding SYNGAP1-related neurodevelopmental disorders. Neurobiol Dis 171:105123. https://doi.org/10.1016/j.nbd.2022.105123 Guo X, Chen L, Yin J, Wang T, Huang G (2021) Molecular mechanisms regulating the Ras/ERK signaling pathway in synaptic plasticity: role of SynGAP. Neurosci Lett 739:135400. https://doi.org/10.1016/j.neulet.2020.135400 Hamdan FF, Gauthier J, Spiegelman D et al (2009) Mutations in SYNGAP1 in autosomal nonsyndromic mental retardation. N Engl J Med 360:599–605. https://doi.org/10.1056/NEJMoa0805392 Hamdan FF, Daoud H, Piton A et al (2011) De novo SYNGAP1 mutations in nonsyndromic intellectual disability and autism. Biol Psychiatry 69(9):898–901. https://doi.org/10.1016/j.biopsych.2010.11.015 Harrison SM, Biesecker LG, Rehm HL (2023) ClinGen expert clinical validity curation of 518 gene–disease pairs. Genet Med 25(4):795–804. https://doi.org/10.1016/j.gim.2022.12.007 Holder JL Jr et al (2018) Precision medicine for genetic epilepsies: integrating genomics into clinical care. Neurotherapeutics 15(4):993–1001. https://doi.org/10.1007/s13311-018-00679-z Ioannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877–885. https://doi.org/10.1016/j.ajhg.2016.08.016 Ivenshitz M, Segal M (2010) Neuregulin-1 induces expression of α-actinin and alters dendritic spine morphology in hippocampal neurons via Ras/ERK pathway. J Neurochem 113(4):862–872. https://doi.org/10.1111/j.1471-4159.2010.06657.x Jaganathan K, Panagiotopoulou G, McRae JF et al (2019) Predicting splicing from primary sequence with deep learning. Cell 176(3):535–548e24. https://doi.org/10.1016/j.cell.2018.12.015 Jensen FE, Bonde L (2020) Integrating molecular and imaging modalities in understanding epilepsy. Epilepsy Res 163:106333. https://doi.org/10.1016/j.eplepsyres.2020.106333 Jimenez-Gomez A, Nair DR, Myrick LK, Holder JL Jr et al (2019) SYNGAP1 haploinsufficiency and neurodevelopmental disorders: emerging clinical features. Epilepsy Res 149:31–37. https://doi.org/10.1016/j.eplepsyres.2018.11.009 Jimenez-Gomez A, Holder JL Jr et al (2023) Comprehensive phenotypic analysis of SYNGAP1-related disorders suggests differential effects of variant type. Genet Med 25(4):100006. https://doi.org/10.1016/j.gim.2023.100006 Karczewski KJ, Francioli LC, Tiao G et al (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581(7809):434–443. https://doi.org/10.1038/s41586-020-2308-7 Karaca E, Harel T, Pehlivan D et al (2018) Genes that affect brain structure and function identified by rare variant analyses of Mendelian neurodevelopmental disorders. Am J Hum Genet 102(4):695–712. https://doi.org/10.1016/j.ajhg.2018.03.014 Kazdoba TM, Leach PT, Crawley JN (2016) Behavioral phenotypes of genetic mouse models of autism. Genes Brain Behav 15(1):7–26. https://doi.org/10.1111/gbb.12256 Kilinc M, Gökçe O, Rumbaugh G (2018) The synaptic pathology of autism spectrum disorders. Nat Rev Neurosci 19:275–287. https://doi.org/10.1038/nrn.2018.10 Kilinc M et al (2021) Species-conserved SYNGAP1 phenotypes. Brain 144(7):2140–2150. https://doi.org/10.1093/brain/awab130 Komiyama NH, Watabe AM, Carlisle HJ, Porter K, Charlesworth P, Monti J et al (2002) SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the hippocampus. Neuron 35(6):903–914. https://doi.org/10.1016/S0896-6273(02)00857-5 Li B, Sun P, Gao Z, Huang L, Zhang Z, Zhang H (2023) Identification and functional characterization of de novo SYNGAP1 variant causing intellectual disability. Front Genet 14:1270175. https://doi.org/10.3389/fgene.2023.1270175 McRae JF, Clayton S, Fitzgerald TW et al (2017) Prevalence and architecture of de novo mutations in developmental disorders. Nature 542(7642):433–438. https://doi.org/10.1038/nature21062 Meili F et al (2021) Multi-parametric analysis of 57 SYNGAP1 variants reveals functional diversity across variants and assays. Epilepsy Res 174:106650. https://doi.org/10.1016/j.eplepsyres.2021.106650 Michaelson JJ, Shi Y, Gujral M, Zheng H, Malhotra D, Jin X et al (2018) Functional disruption of SYNGAP1 causes synaptic signaling defects and autism-associated phenotypes. Cell 173(3):775–789e17. https://doi.org/10.1016/j.cell.2018.02.052 Mignot C, von Stülpnagel C, Nava C, Ville D, Sanlaville D, Lesca G et al (2016) Genetic and neurodevelopmental spectrum of SYNGAP1-associated intellectual disability and epilepsy. J Med Genet 53(8):511–522. https://doi.org/10.1136/jmedgenet-2015-103451 Newman S, Hermetz KE, Weckselblatt B, Rudd MK (2016) Next-generation sequencing of duplication CNVs reveals that most are tandem and some create fusion genes at breakpoints. Am J Hum Genet 98(5):967–983. https://doi.org/10.1016/j.ajhg.2016.03.016 Oksanen J, Blanchet FG, Friendly M et al (2020) vegan: Community Ecology Package. R package version 2.5-7 Ozkan ED, Creson TK, Kramár EA, Rojas C, Seese RR, Babyan AH et al (2014) Reduced cognition in SYNGAP1 mutants is caused by isolated damage within developing forebrain excitatory neurons. Neuron 82(6):1317–1333. https://doi.org/10.1016/j.neuron.2014.04.048 Ozkan ED, Creson TK et al (2015) Reduced cognition in Syngap1 mutants. J Neurosci 35(47):15834–15842. https://doi.org/10.1523/JNEUROSCI.0974-15.2015 Penn AC, Zhang CL, Georges F et al (2017) Hippocampal LTP is enhanced by inhibiting the Ras/ERK pathway via SynGAP modulation. Mol Ther 25(6):1340–1349. https://doi.org/10.1016/j.ymthe.2017.03.017 Richards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants. Genet Med 17(5):405–424. https://doi.org/10.1038/gim.2015.30 Rumbaugh G, Adams JP, Kim JH, Huganir RL (2006) SynGAP regulates synaptic strength and mitogen-activated protein kinases in cultured neurons. Proc Natl Acad Sci U S A 103(12):4344–4349. https://doi.org/10.1073/pnas.0600084103 Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303 Srivastava S, Cohen JS, Vernon H et al (2014) Clinical whole exome sequencing in child neurology practice. Ann Neurol 76(4):473–483. https://doi.org/10.1002/ana.24251 Thomas RH et al (2021) Preclinical development of gene therapy for SYNGAP1 haploinsufficiency. Mol Ther 29(3):1326–1339. https://doi.org/10.1016/j.ymthe.2020.12.023 Vissers LELM, Holder JL Jr, Jimenez-Gomez A et al (2023) Supportive and symptomatic therapies in SYNGAP1-related neurodevelopmental disorders. Neurotherapeutics 20(3):812–829. https://doi.org/10.1007/s13311-023-01379-z Vlaskamp DRM, Callenbach PMC, Rump P, Fișeșan C, Bahi-Buisson N, den Hollander NS et al (2019) SYNGAP1 encephalopathy: a distinctive generalized developmental and epileptic encephalopathy. Neurology 92(2):e96–e107. https://doi.org/10.1212/WNL.0000000000006729 Weldon M, Kilinc M, Holder JL Jr et al (2018) Clinical variability in SYNGAP1-related intellectual disability and epilepsy. Epilepsia 59(5):e56–e63. https://doi.org/10.1111/epi.14046 Wiltrout K, Holder JL Jr et al (2024) Comprehensive phenotypes of patients with SYNGAP1-related disorder reveal high rates of epilepsy and autism. Epilepsia 65:1428–1438. https://doi.org/10.1111/epi.17913 World Medical Association (2013) Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191–2194. https://doi.org/10.1001/jama.2013.281053 Yang X, Wang L, Zhou Y et al (2021) Genetic modifiers and phenotypic variability in neurodevelopmental disorders: insights from clinical cohorts. Front Neurosci 15:642563. https://doi.org/10.3389/fnins.2021.642563 Zeng T, Li YI (2022) Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol 23:103. https://doi.org/10.1186/s13059-022-02664-4 Zhang Y et al (2021) Longitudinal neuroimaging in SYNGAP1-related disorders reveals disrupted connectivity. Brain 144(12):3570–3585. https://doi.org/10.1093/brain/awab285 Gamache TR, Kong E, Kaczorowski CC (2023) Recent advances in understanding SYNGAP1-related neurodevelopmental disorders. Neurobiol Dis 171:105123. https://doi.org/10.1016/j.nbd.2022.105123 Clement JP, Aceti M, Creson TK, Ozkan ED, Shi Y, Reish NJ et al (2012) Pathogenic SYNGAP1 mutations impair cognitive development by disrupting maturation of dendritic spine synapses. Cell 151(4):709–723. https://doi.org/10.1016/j.cell.2012.08.045 Araki Y, Zeng M, Zhang M, Huganir RL (2020) Structural basis of synaptic Ras GTPase-activating protein function in learning and memory. Nat Commun 11:4392. https://doi.org/10.1038/s41467-020-18104-5 Ozkan ED, Creson TK, Kramár EA, Rojas C, Seese RR, Babyan AH et al (2014) Reduced cognition in SYNGAP1 mutants is caused by isolated damage within developing forebrain excitatory neurons. Neuron 82(6):1317–1333. https://doi.org/10.1016/j.neuron.2014.04.048 Guo X, Chen L, Yin J, Wang T, Huang G (2021) Molecular mechanisms regulating the Ras/ERK signaling pathway in synaptic plasticity: role of SynGAP. Neurosci Lett 739:135400. https://doi.org/10.1016/j.neulet.2020.135400 Komiyama NH, Watabe AM, Carlisle HJ, Porter K, Charlesworth P, Monti J et al (2002) SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the hippocampus. Neuron 35(6):903–914. https://doi.org/10.1016/S0896-6273(02)00857-5 Ivenshitz M, Segal M (2010) Neuregulin-1 induces expression of α-actinin and alters dendritic spine morphology in hippocampal neurons via Ras/ERK pathway. J Neurochem 113(4):862–872. https://doi.org/10.1111/j.1471-4159.2010.06657.x Rumbaugh G, Adams JP, Kim JH, Huganir RL (2006) SynGAP regulates synaptic strength and mitogen-activated protein kinases in cultured neurons. Proc Natl Acad Sci U S A 103(12):4344–4349. https://doi.org/10.1073/pnas.0600084103 Vlaskamp DRM, Callenbach PMC, Rump P, Fișeșan C, Bahi-Buisson N, den Hollander NS et al (2019) SYNGAP1 encephalopathy: a distinctive generalized developmental and epileptic encephalopathy. Neurology 92(2):e96–e107. https://doi.org/10.1212/WNL.0000000000006729 Mignot C, von Stülpnagel C, Nava C, Ville D, Sanlaville D, Lesca G et al (2016) Genetic and neurodevelopmental spectrum of SYNGAP1-associated intellectual disability and epilepsy. J Med Genet 53(8):511–522. https://doi.org/10.1136/jmedgenet-2015-103451 Kilinc M, Gökçe O, Rumbaugh G (2018) The synaptic pathology of autism spectrum disorders. Nat Rev Neurosci 19:275–287. https://doi.org/10.1038/nrn.2018.10 Aceti M, Creson TK, Vaissiere T, Rojas C, Huang WC, Wang YX et al (2015) Syngap1 haploinsufficiency damages a postnatal critical period of pyramidal cell structural maturation linked to cortical circuit assembly. Biol Open 4(6):659–668. https://doi.org/10.1242/bio.20148418 Berryer MH, Hamdan FF, Klitten LL et al (2013) Mutations in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum Mutat 34(2):385–393. https://pubmed.ncbi.nlm.nih.gov/23161826/ Hamdan FF, Gauthier J, Spiegelman D et al (2009) Mutations in SYNGAP1 in autosomal nonsyndromic mental retardation. N Engl J Med 360:599–605 Hamdan FF, Daoud H, Piton A et al (2011) De novo SYNGAP1 mutations in nonsyndromic intellectual disability and autism. Biol Psychiatry 69(9):898–901 Ozkan ED, Creson TK et al (2015) Reduced cognition in Syngap1 mutants. J Neurosci 35(47):15834–15842 Penn AC, Zhang CL, Georges F et al (2017) Hippocampal LTP is enhanced by inhibiting the Ras/ERK pathway via SynGAP modulation. Mol Ther 25(6):1340–1349 Kilinc M et al (2021) Species-conserved SYNGAP1 phenotypes. Brain 144(7):2140–2150 World Medical Association (2013) Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191–2194 Richards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants. Genet Med 17(5):405–424 Harrison SM, Biesecker LG, Rehm HL (2023) ClinGen expert clinical validity curation of 518 gene–disease pairs. Genet Med 25(4):795–804 Jaganathan K, Panagiotopoulou G, McRae JF et al (2019) Predicting splicing from primary sequence with deep learning. Cell 176(3):535–548e24 Zeng T, Li YI (2022) Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol 23:103. https://pubmed.ncbi.nlm.nih.gov/35449021/ Ioannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877–885 Karczewski KJ, Francioli LC, Tiao G et al (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581(7809):434–443 Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18(1):117–143 Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26(1):32–46 Oksanen J, Blanchet FG, Friendly M et al (2020) vegan: Community Ecology Package. R package version 2.5-7 Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504 Carvill GL, Regan BM, Yendle SC et al (2013) Mutations in the GTPase-activating protein SynGAP cause intellectual disability and epilepsy. Am J Hum Genet 92(1):89–103 Jimenez-Gomez A, Nair DR, Myrick LK, Holder JL Jr et al (2019) SYNGAP1 haploinsufficiency and neurodevelopmental disorders: emerging clinical features. Epilepsy Res 149:31–37 Holder JL Jr et al (2018) Precision medicine for genetic epilepsies: integrating genomics into clinical care. Neurotherapeutics 15(4):993–1001 Vissers LELM, Holder JL Jr, Jimenez-Gomez A et al (2023) Supportive and symptomatic therapies in SYNGAP1-related neurodevelopmental disorders. Neurotherapeutics 20(3):812–829 Jimenez-Gomez A et al (2023) Haploinsufficiency of SYNGAP1 underlies neurodevelopmental disorders. Genet Med 25(4):795–804 Wiltrout K, Holder JL Jr et al (2024) Comprehensive phenotypes of patients with SYNGAP1-related disorder reveal high rates of epilepsy and autism. Epilepsia 65:1428–1438. https://doi.org/10.1111/epi.17913 Thomas RH et al (2021) Preclinical development of gene therapy for SYNGAP1 haploinsufficiency. Mol Ther 29(3):1326–1339 Michaelson JJ, Shi Y, Gujral M, Zheng H, Malhotra D, Jin X et al (2018) Functional disruption of SYNGAP1 causes synaptic signaling defects and autism-associated phenotypes. Cell 173(3):775–789e17 Karaca E, Harel T, Pehlivan D et al (2018) Genes that affect brain structure and function identified by rare variant analyses of Mendelian neurodevelopmental disorders. Am J Hum Genet 102(4):695–712 McRae JF, Clayton S, Fitzgerald TW et al (2017) Prevalence and architecture of de novo mutations in developmental disorders. Nature 542(7642):433–438 Newman S, Hermetz KE, Weckselblatt B, Rudd MK (2016) Next-generation sequencing of duplication CNVs reveals that most are tandem and some create fusion genes at breakpoints. Am J Hum Genet 98(5):967–983 Eising E, Carrion-Castillo A, Vino A et al (2019) A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Neuroimage Clin 23:101842 Jensen FE, Bonde L (2020) Integrating molecular and imaging modalities in understanding epilepsy. Epilepsy Res 163:106333 Weldon M, Kilinc M, Holder JL Jr et al (2018) Clinical variability in SYNGAP1-related intellectual disability and epilepsy. Epilepsia 59(5):e56–e63 Meili F et al (2021) Multi-parametric analysis of 57 SYNGAP1 variants reveals functional diversity across variants and assays. Epilepsy Res 174:106650. https://pubmed.ncbi.nlm.nih.gov/33308442/ Zhang Y et al (2021) Longitudinal neuroimaging in SYNGAP1-related disorders reveals disrupted connectivity. Brain 144(12):3570–3585 Li B, Sun P, Gao Z, Huang L, Zhang Z, Zhang H (2023) Identification and functional characterization of de novo SYNGAP1 variant causing intellectual disability. Front Genet 14:1270175. https://doi.org/10.3389/fgene.2023.1270175 Yang X, Wang L, Zhou Y et al (2021) Genetic modifiers and phenotypic variability in neurodevelopmental disorders: insights from clinical cohorts. Front Neurosci 15:642563. https://doi.org/10.3389/fnins.2021.642563 Children’s Hospital of Philadelphia (CHOP) Researchers Identify Instances of SYNGAP1-Related Disorders Caused by Inherited Genetic Variants. News Release; June 9, 2025. Available from: https://www.chop.edu/news/childrens-hospital-philadelphia-researchers-identify-instances-syngap1-related-disorders Mayo Clinic (Elsevier Pure). Mutations in SYNGAP1 Cause Intellectual Disability, Autism, and a Specific Form of Epilepsy by Inducing Haploinsufficiency. Portal record (2013) Available from: https://mayoclinic.elsevierpure.com/en/publications/mutations-in-syngap1-cause-intellectual-disability-autism-and-a-s Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.Molecularcharacteriscticofthecohort.xlsx Table2A.NeurologicalanddevelopmentalfeaturesofthePolishSYNGAP1group..docx Table2B.EpilepsyandEEGparametersofthePolishSYNGAP1group.docx Table2D.EarlyonsetandadditionalfeaturesofthePolishSYNGAP1group.docx Table3.Clinicalcharacteristicsofthecohort.docx Table4.VariantClassandmajorclinicalscores.docx SupplementaryTable1a.Detailedclinicalcharacteristicofthecohort121.docx SupplementaryTable1b.Clinicalcharacteristicsofthecohort2230.docx 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-7635804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528337143,"identity":"a046eca7-1fe3-4ff4-815d-0df9cfe55052","order_by":0,"name":"ALEKSANDRA JEZELA-STANEK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACxgYg8YDBBsRsPEC8lgSGNDCTOC1gkMBwGEwTp4W5vf3hg4Sa83Zr2w8DbamxiSbssJ4DyQYJx24nbzuTCNRyLC23gaCWGQnHJBLYbiebHQBqYWw4TISW+Q/bfyT8O5dsdv4hsVpmMLMxJLYdsDO7QbQtPWnMEol9yQlmN4C2JBDjF8P24w8/fPhmZ292Pv3hgw81NkRogapIBNMJhJSDgDyUtidG8SgYBaNgFIxQAAD8nUqNUrwL1AAAAABJRU5ErkJggg==","orcid":"","institution":"Instytut Gruźlicy i Chorób Płuc","correspondingAuthor":true,"prefix":"","firstName":"ALEKSANDRA","middleName":"","lastName":"JEZELA-STANEK","suffix":""},{"id":528337144,"identity":"2476d0b4-12eb-4a82-bf9c-da1a72b3fdca","order_by":1,"name":"TOMASZ SKALSKI","email":"","orcid":"","institution":"BIOTECHNOLOGY CENTRE, SILESIAN UNIVERSITY OF TECHNOLOGY","correspondingAuthor":false,"prefix":"","firstName":"TOMASZ","middleName":"","lastName":"SKALSKI","suffix":""},{"id":528337145,"identity":"d0bc5a97-9802-45ec-a125-4ec591f8d767","order_by":2,"name":"KAROLINA CHWIAŁKOWSKA","email":"","orcid":"","institution":"IMAGENE.ME SA","correspondingAuthor":false,"prefix":"","firstName":"KAROLINA","middleName":"","lastName":"CHWIAŁKOWSKA","suffix":""}],"badges":[],"createdAt":"2025-09-17 05:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7635804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7635804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93726344,"identity":"c630425f-b64d-4a4b-993d-2c4abb6f14a1","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37250,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.SchematicrepresentationoftheSYNGAP1genevariantsidentyfiedinthestudy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/289eee886c785325c342d9d4.jpg"},{"id":93726331,"identity":"a46a2dc6-953e-4bda-bffa-4ab0b722994d","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4271952,"visible":true,"origin":"","legend":"","description":"","filename":"SYNGAP1HG.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/a28564b766e080170f472f3d.docx"},{"id":93730218,"identity":"9cdf6758-cd62-4e8e-8955-68cc801716c2","added_by":"auto","created_at":"2025-10-17 02:19:33","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":946216,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/269ab2a1ca14026d3848eb51.jpg"},{"id":93726330,"identity":"af7dbb55-a8ce-4c8d-9e0c-4e3d46669225","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69579,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/ef7f3e68aa08ad5c6ab0256c.jpg"},{"id":93726341,"identity":"0ae1e0cb-b824-45b4-8072-241a0593b8f8","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20736,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.Molecularcharacteriscticofthecohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/2afa31047db3511c35d51260.xlsx"},{"id":93726355,"identity":"eae16019-8b5d-4291-8a81-86f0b847c917","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2878022,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/248d0b6c1058d1d30c8ff35b.jpg"},{"id":93728573,"identity":"f7f59f10-c98c-4a30-b0e7-9b9b52263fde","added_by":"auto","created_at":"2025-10-17 02:11:33","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16565,"visible":true,"origin":"","legend":"","description":"","filename":"Table2A.NeurologicalanddevelopmentalfeaturesofthePolishSYNGAP1group..docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/a85493d5c24d42d376f588cb.docx"},{"id":93728578,"identity":"4a2dae55-9b67-4630-b04f-6d7da3f0d943","added_by":"auto","created_at":"2025-10-17 02:11:34","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3384766,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/d5301b1a5de6c37d5f020041.jpg"},{"id":93726353,"identity":"64253829-0497-4fbe-8e74-4db3fffe1f2f","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16433,"visible":true,"origin":"","legend":"","description":"","filename":"Table2B.EpilepsyandEEGparametersofthePolishSYNGAP1group.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/145a07b2d12f22cfc5d0aa8a.docx"},{"id":93726358,"identity":"d2a2cc6f-f286-4e62-956b-195bb99d143a","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16273,"visible":true,"origin":"","legend":"","description":"","filename":"Table2D.EarlyonsetandadditionalfeaturesofthePolishSYNGAP1group.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/43993707f2436eff493e2540.docx"},{"id":93728575,"identity":"91358baa-53a7-458d-8702-5b9ffb7a9785","added_by":"auto","created_at":"2025-10-17 02:11:33","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17648,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.Clinicalcharacteristicsofthecohort.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/841d6434fb1097ff135a7a56.docx"},{"id":93728574,"identity":"0e6a21c9-6d02-4e1c-bc50-e2463ac9e0e7","added_by":"auto","created_at":"2025-10-17 02:11:33","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16613,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.VariantClassandmajorclinicalscores.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/019567a68d8f72365a70775c.docx"},{"id":93726360,"identity":"58bbe329-4a4e-4f57-9802-da62081c6039","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"json","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4684,"visible":true,"origin":"","legend":"","description":"","filename":"709761cee6d34b9b8de4a388e7cacfdd.json","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/ff3d3591d80c6e63fccca556.json"},{"id":93726351,"identity":"bce4bf98-05cc-45b5-97e5-ff79e8619b99","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61098,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1a.Detailedclinicalcharacteristicofthecohort121.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/d0c81d7909a58fc6fbdf38cf.docx"},{"id":93728571,"identity":"a3c5c8a4-0d67-4660-b2eb-73ed5a981254","added_by":"auto","created_at":"2025-10-17 02:11:33","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55848,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1b.Clinicalcharacteristicsofthecohort2230.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/5dd8ac7ba1f8099995810f9e.docx"},{"id":93726361,"identity":"2dcd04fe-78f1-4b1f-a48a-530c71e4cfbc","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228283,"visible":true,"origin":"","legend":"","description":"","filename":"709761cee6d34b9b8de4a388e7cacfdd1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/3b4cd6e1245e1d0a00f65f85.xml"},{"id":93726348,"identity":"6b2fc698-2e57-4e9d-9b0e-675da88750f1","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37250,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.SchematicrepresentationoftheSYNGAP1genevariantsidentyfiedinthestudy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/f19c26801e972cc01c950b22.jpg"},{"id":93728577,"identity":"e89006c8-63a8-477c-8200-8170b5facf1b","added_by":"auto","created_at":"2025-10-17 02:11:34","extension":"jpg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":946216,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/abc333c12e356708616ecb61.jpg"},{"id":93726336,"identity":"011a4f14-2be8-4676-954b-538f70cd8d34","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69579,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/319e9dc22d86aaedea4e5150.jpg"},{"id":93726347,"identity":"cf49d845-1779-4e4f-bb23-879c758c1ba2","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2878022,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/e909c9f5fa2a8836ed16cbb5.jpg"},{"id":93726369,"identity":"8a0bd224-c9fb-4901-af33-08f7d9d46deb","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"jpg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3384766,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/6332e3bf6dab37647480e98d.jpg"},{"id":93726357,"identity":"eb62d562-6f6a-4a6a-aeac-29a5f2352a20","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16316,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.SchematicrepresentationoftheSYNGAP1genevariantsidentyfiedinthestudy.png","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/e7f9a73f3aea54172265c676.png"},{"id":93726364,"identity":"b3832ae6-3a9a-49a0-9795-c910f4f03241","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95161,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/505ad18342f98dce85db944f.png"},{"id":93726368,"identity":"04655f1b-3159-41e8-9bf0-41915a75b74f","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28934,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/26c2c054eda180c1f04b4b64.png"},{"id":93726366,"identity":"2d49e200-6bbd-4f2b-ba49-45fe1c8cd494","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183522,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/eacbc8383297508d0a45ec7d.png"},{"id":93726362,"identity":"c547e15a-6c5b-4dff-8ad9-3a40a22b0dab","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":322705,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/b22a7328611c303563fe5fc8.png"},{"id":93728579,"identity":"1bf7be26-5121-457e-a654-b3edd544da50","added_by":"auto","created_at":"2025-10-17 02:11:34","extension":"xml","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217099,"visible":true,"origin":"","legend":"","description":"","filename":"709761cee6d34b9b8de4a388e7cacfdd1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/31982f651edf7fbfbf3ab282.xml"},{"id":93726359,"identity":"c65c0781-4bc9-465d-94fb-e66778e04f7f","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":243350,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/ea34e4743c2903e686d0e5f3.html"},{"id":93726328,"identity":"7d30aba9-ca13-4f9b-8e4c-b6d58fb5b57f","added_by":"auto","created_at":"2025-10-17 02:03:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37250,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the \u003cem\u003eSYNGAP1\u003c/em\u003e gene variants identified in this study.\u003c/p\u003e\n\u003cp\u003eVariant type: rhombus, truncating (nonsense and indel frameshifts); squares, splicing; hexagon, missense and in-frame indel. ACMG classification: red, Pathogenic or Likely Pathogenic; navy, VUS (Variant of Unknown Significance). Filled, \u003cem\u003ede novo\u003c/em\u003e; hatched, inherited; contoured, unknown (not tested). Domains and protein motifs are presented according to UniProt (Q96PV0 - SYGP1_HUMAN).\u003c/p\u003e","description":"","filename":"Figure1.SchematicrepresentationoftheSYNGAP1genevariantsidentyfiedinthestudy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/715d2e8aac664e685f2b3162.jpg"},{"id":93726356,"identity":"47b42e32-9adf-43df-a0a8-af262a2696d8","added_by":"auto","created_at":"2025-10-17 02:03:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":946216,"visible":true,"origin":"","legend":"\u003cp\u003eVariability of neurologic symptom evaluations in a patient cohort. Left: Violin plots show the distribution of evaluation scores (from bad to good) for each neurologic symptom. The width of each violin indicates the density of patient scores, with central dots and bars depicting medians and quartiles. Right: Heatmap representing the evaluation scores for each symptom (rows) in individual patients (columns). Color shifts from blue (bad) through green/yellow to red (good) according to the included scale, highlighting intra-cohort heterogeneity and symptom-specific patterns.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/3e966e35c247962dbc2e01ee.jpg"},{"id":93726333,"identity":"0be67dbd-d836-4555-b66e-c0c35b96895a","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69579,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence networking of neurologic symptoms indicating significant relations between each other. The different colous indicate the various nodes numbers. HYPOT – Hypotonia, \u0026nbsp;DELDEV - Delayed development; MI_SEV - Intellectual disability, mild to severe; DEV_REG - Developmental regression; SEIZ – Seizures; EEG_ABN - EEG abnormalities; AEPILDR - Antiepileptic drugs; EPILENC - Epileptic encephalopathy; BRAI_MRI - Normal brain MRI or CT scan; BEH_ MAN - Behavioral Psychiatric Manifestations\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/23326b841223ed7fa82d2442.jpg"},{"id":93726342,"identity":"3b2237b6-3022-4a2b-9fc6-1b5edc578f4c","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2878022,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of patients based on symptom score profiles and variant types using \u0026nbsp;non-metric multidimensional scaling (NMDS) of. A. according to the second grouping strategy applied to symptom scores variants and B. based on the fourth grouping strategy. Variant groups: Truncating (blue polygon), Missense (orange polygon), and Splicing (green polygon, Loss-of-Function (purple polygon) and Missense (orange polygon).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/377cf746b5dd6326a4634b7b.jpg"},{"id":93730220,"identity":"e6c2caef-487a-495c-9563-740b3bd69bb5","added_by":"auto","created_at":"2025-10-17 02:19:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3384766,"visible":true,"origin":"","legend":"\u003cp\u003eRadar plots illustrating mean neurologic symptom evaluations across \u003cem\u003eSYNGAP1\u003c/em\u003e variant classes based on SIMPER analysis. (A) Comparison of symptom scores between Split (blue polygon) and Truncate (green polygon) variant groups according to the second grouping strategy. (B) \u0026nbsp;Comparison of symptom scores between Missense (orange polygon) and Loss-of-Function (blue polygon) variant groups based on the fourth grouping strategy.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/63249c41e78ef03d8c0c0807.jpg"},{"id":108490894,"identity":"ab42f1a1-c687-45d2-8a6e-dc1185ab54fd","added_by":"auto","created_at":"2026-05-05 09:49:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4159998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/bc1fcd9f-bd54-4c36-bb39-c76a0a497782.pdf"},{"id":93726332,"identity":"90ffa7b1-c31f-46d4-a406-542ceebcd223","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20736,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.Molecularcharacteriscticofthecohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/c5199f71daa14ce7e87a8953.xlsx"},{"id":93726338,"identity":"ebfd7243-ee87-468a-89eb-1e66f2893d8d","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16565,"visible":true,"origin":"","legend":"","description":"","filename":"Table2A.NeurologicalanddevelopmentalfeaturesofthePolishSYNGAP1group..docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/bb3671651fd0db1107751da1.docx"},{"id":93726340,"identity":"bdd84fa2-8217-4e89-aeb5-9aaaa120ea70","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16433,"visible":true,"origin":"","legend":"","description":"","filename":"Table2B.EpilepsyandEEGparametersofthePolishSYNGAP1group.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/2a122bba2e438d7ed022d1bf.docx"},{"id":93726346,"identity":"8fe7fa13-f1ae-4f8b-aaee-1a66ed59f1be","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16273,"visible":true,"origin":"","legend":"","description":"","filename":"Table2D.EarlyonsetandadditionalfeaturesofthePolishSYNGAP1group.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/51ed935ec3a67fec27849d2f.docx"},{"id":93730219,"identity":"4d47a186-edf9-49ed-ab24-0b9281247fb6","added_by":"auto","created_at":"2025-10-17 02:19:33","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17648,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.Clinicalcharacteristicsofthecohort.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/73f6634a52eb54e0f4c7e666.docx"},{"id":93730221,"identity":"9b48b6a6-6547-40fa-82ed-2dc2ad4f7974","added_by":"auto","created_at":"2025-10-17 02:19:34","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16613,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.VariantClassandmajorclinicalscores.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/5f563870568b4c9fe0ee5f04.docx"},{"id":93726350,"identity":"219d91b0-dc16-41f1-9202-84bd012a6191","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":61098,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1a.Detailedclinicalcharacteristicofthecohort121.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/d9863f6e485efac08882bdf5.docx"},{"id":93726339,"identity":"94ff1dce-9bb0-470a-9e76-441a427dad1b","added_by":"auto","created_at":"2025-10-17 02:03:33","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":55848,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1b.Clinicalcharacteristicsofthecohort2230.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635804/v1/71a24fcc027eec3b028eb94f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMolecular Characterization and Genotype-phenotype Correlations of SYNGAP1 Variants in a Polish Pediatric Cohort With Neurodevelopmental Disorders \u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Comprehensive study of variants in 30 Polish pediatric patients.\u003c/p\u003e\u003cp\u003e\u0026bull; 50% of identified variants in cohort are novel pathogenic changes.\u003c/p\u003e\u003cp\u003e\u0026bull; Truncating variants linked to severe developmental delay and epilepsy.\u003c/p\u003e\u003cp\u003e\u0026bull; Missense variants show broader symptom variability and milder cognitive impact.\u003c/p\u003e\u003cp\u003e\u0026bull; High epilepsy prevalence with frequent polytherapy treatment in severe cases.\u003c/p\u003e\u003cp\u003e\u0026bull; NMDS and PERMANOVA analyses reveal distinct genotype-phenotype clusters.\u003c/p\u003e\u003cp\u003e\u0026bull; Study confirms haploinsufficiency as the main pathogenic mechanism.\u003c/p\u003e\u003cp\u003e\u0026bull; Emphasizes need for personalized medicine and targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003e\u0026bull; Future work: larger cohorts, international collaborations, and advanced therapies.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eThe \u003cem\u003eSYNGAP1\u003c/em\u003e gene, located on chromosome 6p21.3, encodes the synaptic Ras GTPase-activating protein SynGAP, a critical regulator of synaptic signaling and plasticity (Gamache et al., 2023; Clement et al., 2012). SynGAP is predominantly localized in the postsynaptic density of excitatory neurons, where it modulates multiple intracellular signaling pathways essential for synaptic development and function (Araki et al., 2020; Ozkan et al., 2014). Its principal role involves acting as a Ras GTPase-activating protein, catalyzing the conversion of active Ras-GTP to inactive Ras-GDP, thereby tightly regulating the Ras/ERK (extracellular signal-regulated kinase) signaling cascade (Guo et al., 2021; Komiyama et al., 2002). This pathway is fundamental for activity-dependent synaptic plasticity mechanisms, such as long-term potentiation (LTP), which underlie learning and memory processes (Ivenshitz and Segal, 2010; Rumbaugh et al., 2006).\u003c/p\u003e\n\u003cp\u003ePathogenic variants most commonly result in haploinsufficiency, which manifests as intellectual disability, epilepsy, and autism spectrum disorder (Vlaskamp et al., 2019; Mignot et al., 2016). This diminution disrupts the fine balance of synaptic signaling, leading to excessive Ras/ERK pathway activation and subsequent impairment of synaptic maturation and plasticity (Kilinc et al., 2018; Aceti et al., 2015). Clinically, this manifests as a spectrum of neurodevelopmental disorders characterized by global developmental delay, intellectual disability, epilepsy, and autism spectrum disorder (Berryer et al., 2013; Mignot et al., 2016; Hamdan et al., 2009). More than two-thirds of patients with \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders harbor truncating variants, which are strongly associated with severe neurodevelopmental phenotypes, while a smaller subset carry missense or splicing variants with variable clinical severity (Hamdan et al., 2011; Ozkan et al., 2015).\u003c/p\u003e\n\u003cp\u003eDespite the absence of curative treatments currently, ongoing research is exploring targeted therapeutic strategies, including gene therapy aimed at restoring \u003cem\u003eSYNGAP1\u003c/em\u003e expression, and pharmacological modulation of downstream pathways such as the Ras/ERK cascade (Penn et al., 2017; Kilinc et al., 2021). Early behavioral and cognitive interventions also remain vital in improving functional outcomes for affected individuals (Vlaskamp et al., 2019).\u003c/p\u003e\n\u003cp\u003eThis study aims to comprehensively characterize the spectrum of pathogenic and likely pathogenic variants identified in the \u003cem\u003eSYNGAP1\u003c/em\u003e gene within a nationwide cohort of 30 Polish pediatric patients diagnosed with \u003cem\u003eSYNGAP1\u003c/em\u003e-related neurodevelopmental disorders. Specifically, the objectives include detailed molecular analysis of identified genetic variants to define their nature, location, and predicted functional impact using contemporary bioinformatic and clinical classification guidelines. Concurrently, the study seeks to systematically collect and quantitatively assess detailed clinical and neurological phenotypes\u0026mdash;including developmental, epileptic, and behavioral features\u0026mdash;using a structured symptom scoring system. By integrating genetic and phenotypic data, the study endeavors to investigate genotype-phenotype correlations and elucidate patterns of symptom manifestations associated with different variation types (e.g., truncating, missense, splice-site). These insights refine our understanding of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders and provide a framework for patient stratification in upcoming clinical trials and targeted therapeutic approaches. Ultimately, the research supports the development of stratified and personalized clinical management strategies and informs ongoing translational therapeutic efforts such as gene therapy and targeted pharmacological interventions for this rare but impactful disorder.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003ePatient Cohort Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a Polish nationwide cohort of 30 children diagnosed with Intellectual Developmental Disorder, Autosomal Dominant 5 (MRD5), caused by pathogenic or likely pathogenic variants in the \u003cem\u003eSYNGAP1\u003c/em\u003e gene (Vlaskamp et al., 2019; Mignot et al., 2016). All participants were of Polish origin and were evaluated in specialized pediatric neurology or medical genetics departments across Poland, consistent with standards applied in comparable international neurogenetic cohorts (Berryer et al., 2013; Hamdan et al., 2011). Genetic testing was performed as part of routine diagnostic services in accredited nationwide genetics laboratories following national clinical and regulatory frameworks (Hamdan et al., 2009). Informed consent was obtained from the patients or their parents/legal guardians in compliance with national regulations and the Declaration of Helsinki (World Medical Association, 2013).\u003c/p\u003e\n\u003cp\u003eClinical and genetic data were meticulously collected through structured medical questionnaires and standardized data tables completed by attending physicians or parents, ensuring comprehensive phenotypic documentation (Mignot et al., 2016). Specific cohort characteristics included:\u003c/p\u003e\n\u003cp\u003eCohort size: 30 children\u003c/p\u003e\n\u003cp\u003eGender distribution: 15 males and 15 females\u003c/p\u003e\n\u003cp\u003eAge at clinical diagnosis: 2 to 15 years\u003c/p\u003e\n\u003cp\u003eMolecular confirmation: All cases harbored pathogenic or likely pathogenic \u003cem\u003eSYNGAP1\u003c/em\u003e variants consistent with the MRD5 phenotype, Table 1 (Vlaskamp et al., 2019; Hamdan et al., 2011)\u003c/p\u003e\n\u003cp\u003eInheritance pattern: The majority of variants were \u003cem\u003ede novo\u003c/em\u003e, confirmed by parental testing or inferred through clinical and laboratory data (Berryer et al., 2013; Mignot et al., 2016).\u003c/p\u003e\n\u003cp\u003eInclusion criteria required confirmed molecular diagnosis, availability of detailed clinical records, and parental consent for anonymized data usage in scientific research, aligning with ethical and clinical best practices applied globally in rare neurodevelopmental disorders (Hamdan et al., 2009; Vlaskamp et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSymptom assessment and Scoring System (SS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo achieve a structured and comparable characterization of neurological and behavioral symptoms across the cohort, a detailed numerical point-based scoring system was implemented. This system quantified the severity and presence of key clinical features by assigning predefined numerical values to each symptom domain, enabling standardized assessment across patients with variable clinical manifestations (clinical details are in Supplementary Table1a, Table 1b). The scoring framework encompassed a broad range of neurological and developmental features, including microcephaly, torticollis, hypotonia (graded from normal tone to reduced central tone and increased peripheral tone), developmental delay, intellectual disability (ranging from none to severe for children aged six years or older), and developmental regression.\u003c/p\u003e\n\u003cp\u003eAdditional parameters captured epilepsy-related characteristics, such as seizure type (categorized from absence to tonic-clonic seizures and seizures exclusively during sleep), EEG abnormalities (scored from normal to generalized or focal atypical discharges), antiepileptic drug use (binary assessment of medication count), and presence of epileptic encephalopathy. Behavioral and imaging features were also systematically recorded, with brain imaging results classified by the presence or absence of structural abnormalities, and autism spectrum disorder (ASD) and related behavioral phenotypes graded along a spectrum from no signs to confirmed autism and behavioral issues.\u003c/p\u003e\n\u003cp\u003eTo facilitate meaningful inter-patient comparisons and subsequent multivariate analyses, each patient\u0026rsquo;s aggregate symptom scores were normalized by dividing the individual domain scores by the respective maximum possible score within that domain, thereby scaling all scores to a common range between 0 and 1. This normalization procedure ensured that symptom severity measurements were directly comparable across diverse clinical domains and allowed for robust statistical analyses of genotype-phenotype correlations and symptom clustering within the cohort. This quantitative approach was further supplemented by qualitative descriptions obtained through parental interviews and clinical observations to fully capture complex neurodevelopmental and behavioral presentations that may elude numerical scoring alone (Tables 2A-1D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA detailed clinical record was completed for each patient using this comprehensive framework, integrating both quantitative symptom profiles and supporting qualitative descriptions where applicable. This structured approach facilitated robust intra-cohort comparisons, enabling the identification of symptom patterns and severities within the group (Mignot et al., 2016; Vlaskamp et al., 2019). Moreover, the standardized data collection and scoring system allowed for rigorous analysis of genotype\u0026ndash;phenotype correlations, providing insights into how distinct \u003cem\u003eSYNGAP1\u003c/em\u003e variants contribute to phenotypic heterogeneity (Hamdan et al., 2011; Berryer et al., 2013).\u003c/p\u003e\n\u003cp\u003eImportantly, this methodology supports extrapolation to future meta-analyses of rare neurodevelopmental disorders by offering a scalable and reproducible assessment model that can harmonize data across international cohorts (Mignot et al., 2016; Hamdan et al., 2009). Where necessary, subjective clinical observations and detailed parent interviews supplemented the numerical scoring to capture complex neurodevelopmental or behavioral presentations that are not easily quantifiable by standardized scales alone, reflecting best practices in phenotypic characterization within rare disease research (Srivastava et al., 2014; Kazdoba et al., 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic variant molecular assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic variants identified in the diagnostic setting and deemed causal for patient symptoms by the attending physician underwent further rigorous evaluation by experienced clinical genomic scientists. Molecular analyses and formal variant classifications were conducted following the most current standards as outlined by the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) guidelines (Richards et al., 2015; Harrison et al., 2023). All \u003cem\u003eSYNGAP1\u003c/em\u003e variants were annotated against the MANE Select transcript NM_006772.3 to ensure consistency and accuracy in variant reporting. Genomic coordinates were mapped using the Genome Reference Consortium Human Reference sequence version 38 (GRCh38/hg38). To assess the potential impact of variants on mRNA splicing, in silico prediction tools SpliceAI and Pangolin were employed, leveraging deep learning-based models to robustly predict splicing alterations (Jaganathan et al., 2019; Zhang et al., 2021). The possible effect of missense variants was further evaluated using the REVEL metapredictor, which integrates multiple tools for improved predictive accuracy of deleterious missense variants (Ioannidis et al., 2016). Population allele frequencies were examined in the Genome Aggregation Database (gnomAD) version 4.1.0 (Karczewski et al., 2020). Variant interpretation was supported by aided by information gathered from comprehensive literature and case report reviews, as well as several clinical variant databases, including ClinVar (NCBI), the Leiden Open Variation Database (LOVD), and the Human Gene Mutation Database (HGMD public).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis aimed to elucidate differences in neurologic symptom profiles among \u003cem\u003eSYNGAP1\u003c/em\u003e variant subgroups through a comprehensive multivariate framework. Non-metric multidimensional scaling (NMDS) was employed to reduce the multidimensional symptom score data into a lower-dimensional space, thereby enabling visualization of complex relationships and patient classification based on symptom similarity (Clarke, 1993). NMDS was performed using the Bray\u0026ndash;Curtis dissimilarity metric, which is well-suited for ecological and clinical datasets containing mixed data types and accounts for both presence-absence and abundance information. To rigorously assess the statistical significance of group separations observed in the NMDS ordination, Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted using the adonis function available in the vegan package for R (Anderson, 2001; Oksanen et al., 2020). PERMANOVA tests whether the centroids of predefined variant groups differ significantly in the multidimensional symptom space, with significance evaluated via 999 permutations to robustly control type I error rates.\u003c/p\u003e\n\u003cp\u003eFurther, Similarity Percentage Analysis (SIMPER) was utilized to decompose the overall dissimilarity between variant groups and to identify individual symptom variables contributing most substantially to the observed differences (Clarke, 1993). Mean symptom evaluation scores across variant classes were compared to pinpoint neurologic features driving group differentiation, offering insights into specific phenotypic domains affected by different variant types. All analyses were performed using R (version 4.5.1) with the vegan community ecology package, with a standard alpha threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 applied to determine statistical significance throughout the study.\u003c/p\u003e\n\u003cp\u003eThe co-occurrence patterns of neurologic symptoms within the \u003cem\u003eSYNGAP1\u003c/em\u003e patient cohort were analyzed using Cytoscape version 3.10.3, a widely used open-source platform for complex network visualization and analysis (Shannon et al., 2003). Symptom co-occurrence networks were constructed by quantifying pairwise associations between symptoms based on their simultaneous presence or severity levels across patients. These associations were translated into edges connecting nodes representing individual symptoms, enabling the visualization of symptom interrelationships.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e1. General clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cohort comprised 30 Polish children diagnosed with \u003cem\u003eSYNGAP1\u003c/em\u003e-related intellectual developmental disorder, autosomal dominant 5 (MRD5), with equal gender distribution (15 males, 15 females). The age at genetic diagnosis ranged from 1.5 to 13 years, with a median age of approximately 4 years. All identified \u003cem\u003eSYNGAP1\u003c/em\u003e variants (30/30, 100%) occurred \u003cem\u003ede novo\u003c/em\u003e, confirming the sporadic nature of this condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeurological manifestations\u003c/strong\u003e were nearly universal in our cohort. Hypotonia was present in 28/30 patients (93.3%), typically presenting as reduced muscle tone in the central axis with variable involvement of the extremities. Significant developmental delays were observed in 27/30 patients (90%), while 3 patients exhibited minor delays. Intellectual disability was documented in all patients over 6 years of age, with severity distribution as follows: mild in 3 patients (10%), moderate in 18 patients (60%), and severe in 6 patients (20%). Developmental regression was observed in 12/30 patients (40.0%), often triggered by infections or during periods of therapeutic discontinuity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpileptic manifestations\u003c/strong\u003e were prominent features, with seizures occurring in 26/30 patients (86.7%). The most common seizure types included absence seizures and myoclonic seizures, often presenting in combination. EEG abnormalities were documented in 25/30 patients (83.3%), predominantly showing generalized epileptiform changes. Epileptic encephalopathy was diagnosed in 22/30 patients (73.3%), reflecting the severe impact of seizure activity on neurodevelopmental progression. Antiepileptic drug management varied widely, with valproic acid and lamotrigine being the most commonly prescribed medications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral and psychiatric features\u003c/strong\u003e were extensively documented, with autism spectrum disorder (ASD) diagnosed in 24/30 patients (80%). Common behavioral abnormalities included stereotypic movements, sensory integration disorders, sleep disturbances, aggressive behaviors, and hypersensitivity to auditory stimuli. Many patients exhibited characteristic autistic behaviors such as hand flapping, fixations on specific objects or activities, and difficulties with social interaction and communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical characteristics\u003c/strong\u003e showed microcephaly in only 2/30 patients (6.7%), indicating that head circumference is typically within normal limits in this population. Torticollis was rare, present in only 1 patient. Brain MRI findings were predominantly normal (15/30 patients) or showed minor non-specific changes (12/30 patients), with only 3 patients displaying serious malformations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional clinical features\u003c/strong\u003e included various ophthalmologic abnormalities (nystagmus, astigmatism, hyperopia), gastrointestinal issues (feeding difficulties, gastroesophageal reflux, constipation), sleep disorders, and altered pain sensitivity. Several patients demonstrated unusual pain tolerance and hypersensitivity to environmental stimuli, particularly loud sounds and bright lights.\u003c/p\u003e\n\u003cp\u003eThe clinical onset typically occurred within the first year of life in most patients, with early signs including hypotonia, feeding difficulties, and delayed developmental milestones. The comprehensive phenotypic spectrum confirms \u003cem\u003eSYNGAP1\u003c/em\u003e haploinsufficiency as a significant cause of neurodevelopmental disability with characteristic epileptic and behavioral manifestations in the Polish pediatric population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u003cem\u003eSYNGAP1\u003c/em\u003e Variants Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this Polish nationwide cohort, we comprehensively evaluated 30 distinct genetic variants classified as causal for \u003cem\u003eSYNGAP1\u003c/em\u003e-related intellectual developmental disorder (MRD5) (Table 1). Each variant was rigorously classified as pathogenic or likely pathogenic according to ACMG/ClinGen guidelines, ensuring high confidence in their clinical relevance. The identified variants encompassed a heterogeneous array of molecular alterations, including nonsense, frameshift, splice-site, and missense, collectively reflecting the established mutational spectrum of \u003cem\u003eSYNGAP1\u003c/em\u003e-associated MRD5. Detailed variant annotations and patient-specific data are provided in the cohort summary tables (Supplementary Tables 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Variant molecular types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTruncating Variants: The majority consisted of truncating variants predicted to introduce premature termination codons, such as nonsense and frameshift changes (e.g., c.155C\u0026gt;A, p.Ser52Ter; c.2993delC, p.Pro978Hisfs99; c.2789del, p.Pro930Leufs147). These variants are anticipated to induce loss-of-function predominantly via mechanisms like nonsense-mediated mRNA decay or generation of severely truncated, non-functional SynGAP proteins.\u003c/p\u003e\n\u003cp\u003eSplice-Site Variants: A subset of patients harbored variants affecting canonical splice donor or acceptor sites or adjacent regulatory regions (e.g., c.664-2A\u0026gt;G, c.388-2A\u0026gt;C), which are predicted to disrupt normal pre-mRNA splicing and consequently diminish functional protein production or alter transcript composition. In silico splice prediction tools support their potential functional consequences.\u003c/p\u003e\n\u003cp\u003eMissense Variants: Pathogenic or likely pathogenic missense changes (e.g., c.1685C\u0026gt;T, p.Pro562Leu; c.1036delG, p.Val346Ter) were less frequent but involve evolutionarily conserved residues within critical functional domains, directly compromising SynGAP protein function through altered enzymatic activity or protein stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Genomic localization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariant type: rhombus, truncating (nonsense and indel frameshifts); squares, splicing; hexagon, missense and in-frame indel. ACMG classification: red, Pathogenic or Likely Pathogenic; navy, VUS (Variant of Unknown Significance). Filled, \u003cem\u003ede novo\u003c/em\u003e; hatched, inherited; contoured, unknown (not tested). Domains and protein motifs are presented according to UniProt (Q96PV0 - SYGP1_HUMAN).\u003c/p\u003e\n\u003cp\u003eThe variants predominantly clustered within functional domains essential for SynGAP activity, notably the Ras GTPase-activating (RasGAP) domain and the C2 lipid-binding domain. This distribution spans multiple exons, underscoring the molecular diversity of pathogenic lesions capable of disrupting \u003cem\u003eSYNGAP1\u003c/em\u003e function (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Zygosity and inheritance patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pathogenic variants identified were heterozygous, consistent with the autosomal dominant inheritance of MRD5. Parental testing or clinical inference confirmed a predominantly \u003cem\u003ede novo\u003c/em\u003e origin of these variants, aligning with global epidemiological data. No evidence for cases of mosaic parental transmission was detected in this cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Description of novel variants, not reported elsewhere\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotably, 15 (50%) of the variants were novel, not previously reported in the literature or public (non-commercial) clinical databases as present in patients with \u003cem\u003eSYNGAP1\u003c/em\u003e-related intellectual developmental disorder:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 3: NM_006772.3:c.3350del p.(Gly1117AlafsTer13)\u0026nbsp;\u003c/strong\u003e- a guanine deletion leading to glycine to alanine change in codon 1117 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 5: NM_006772.3:c.1760_1792del p.(Arg587_Leu598delinsIle)\u0026nbsp;\u003c/strong\u003e- a deletion of 33 nucleotides resulting in 12 amino acids deletion(from codon 587 to 598) and replacement with isoleucine in Ras-GAP domain, covering important protein residues (PM4; PM1_Supporting). Multiple computational evidence predict deleterious effect on the gene product (FATHMM-indel: pathogenic, MutationTaster for indels: deleterious; PP3). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Likely pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 6: NM_006772.3:c.3377del p.(Gly1126ValfsTer4)\u0026nbsp;\u003c/strong\u003e- a single nucleotide deletion leading to glycine to valine change in codon 1126 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 8: NM_006772.3:c.2789del p.(Pro930LeufsTer147)\u003c/strong\u003e - a single nucleotide deletion leading to proline to leucine change in codon 930 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 9: NM_006772.3:c.3794+1G\u0026gt;A p.?\u003c/strong\u003e - a canonical splice site variant affecting the donor site of intron 16, predicted to disrupt normal splicing and leading to frameshift (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 11: NM_006772.3:c.3338del p.(Gly1113AlafsTer17)\u003c/strong\u003e - a single nucleotide deletion leading to glycine to alanine change in codon 1113 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 13: NM_006772.3:c.1253_1256del p.(Lys418SerfsTer21)\u003c/strong\u003e - a deletion of four nucleotides leading to lysine to serine change in codon 418 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 15: NM_006772.3:c.1036del p.(Val346Ter)\u003c/strong\u003e - a single nucleotide deletion resulting in premature stop codon at position 346 (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 17: NM_006772.3:c.2232del p.(Gln744HisfsTer16)\u003c/strong\u003e - a single nucleotide deletion leading to glutamine to histidine change in codon 744 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 21: NM_006772.3:c.1676+5G\u0026gt;A p.?\u0026nbsp;\u003c/strong\u003e- a non-canonical splice site variant predicted by SpliceAI and Pangolin to affect the donor site of intron 10 and disrupt normal splicing (PP3). Leading to the same splicing effect with similar predictions (\u0026lt;10% difference by Pangolin) as a previously established pathogenic variant (PS1_Supporting). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Likely pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 22: NM_006772.3:c.155C\u0026gt;A p.(Ser52Ter)\u003c/strong\u003e - a single nucleotide substitution resulting in premature stop codon at position 52 (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 24: NM_006772.3:c.2933del p.(Pro978HisfsTer99)\u003c/strong\u003e - a single nucleotide deletion leading to proline to histidine change in codon 978 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 25: NM_006772.3:c.2362del p.(Ser788ProfsTer21)\u003c/strong\u003e - a single nucleotide deletion leading to serine to proline change in codon 788 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 29: NM_006772.3:c.439_440del p.(Gln147ThrfsTer4)\u003c/strong\u003e - a two-nucleotide deletion leading to glutamine to threonine change in codon 147 along with frameshift resulting in premature nonsense codon (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient 30: NM_006772.3:c.2115+89_2116-2del p.?\u003c/strong\u003e - a non-coding indel variant covering the canonical acceptor splice site, predicted to disrupt normal splicing and lead to frameshift (PVS1). Absent from GnomAD v4.1.0 (PM2_Supporting). Identified as \u003cem\u003ede novo\u003c/em\u003e in a proband with phenotype associated with \u003cem\u003eSYNGAP1\u003c/em\u003e-related condition (PS2). ACMG classification: Pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u003cem\u003e\u0026nbsp;SYNGAP1\u003c/em\u003e patient subgroup characteristics by variant type\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTruncating variants (e.g., c.155C\u0026gt;A p.Ser52Ter, c.2993delC p.Pro978Hisfs99) demonstrate\u0026nbsp;significant phenotypic variability\u0026nbsp;rather than uniform severity. Patient c.155C\u0026gt;A p.Ser52Ter presented with a\u0026nbsp;relatively mild phenotype\u0026nbsp;characterized by absence of seizures, no developmental regression, excluded autism diagnosis, and intellectual disability ranging from moderate to average. In contrast, patient c.2993delC p.Pro978Hisfs99 exhibited a\u0026nbsp;severe phenotype\u0026nbsp;with absence and myoclonic seizures, severe intellectual disability, and confirmed ASD diagnosis. Notably,\u0026nbsp;neither truncating variant patient demonstrated developmental regression before age 3, contradicting previous assumptions about this phenotypic feature.\u003c/p\u003e\n\u003cp\u003eSplice-site variant (c.388-2A\u0026gt;C) showed a\u0026nbsp;complex phenotype\u0026nbsp;with seizure patterns resembling the severe truncating variant, but uniquely displayed developmental regression (absent in both truncating cases), suggesting\u0026nbsp;distinct pathophysiological mechanisms\u0026nbsp;rather than simple functional equivalency to truncating variants.\u003c/p\u003e\n\u003cp\u003eMissense variants (c.1685C\u0026gt;T p.Pro562Leu, c.895C\u0026gt;T p.Arg299Cys) resulted in\u0026nbsp;moderately severe phenotypes\u0026nbsp;with variable seizure burden, moderate intellectual disability, and behavioral abnormalities. Notably, c.895C\u0026gt;T p.Arg299Cys demonstrated\u0026nbsp;significantly preserved cognitive function\u0026nbsp;with mild intellectual disability, representing the\u0026nbsp;best cognitive outcome\u0026nbsp;in the cohort, while c.1685C\u0026gt;T p.Pro562Leu showed moderate intellectual disability with atonic seizures.\u003c/p\u003e\n\u003cp\u003e3. Non-metric multidimensional scaling data\u003c/p\u003e\n\u003cp\u003e3.1 Neurological symptoms.\u003c/p\u003e\n\u003cp\u003eThe results presented on Fig. 2 indicate substantial variability in neurologic symptom evaluations among the patient cohort, as illustrated by both violin plots and a patient-symptom heatmap. The violin plots reveal that certain symptoms, such as \u0026quot;Normal brain MRI,\u0026quot; cluster strongly toward higher (better) evaluation scores, suggesting that the majority of patients have near-normal findings for this characteristic. In contrast, symptoms like \u0026quot;Epileptic encephalopathy,\u0026quot; \u0026quot;Intellectual disability,\u0026quot; and \u0026quot;Hypotonia\u0026quot; display wider or even multimodal distributions, reflecting a broader spectrum of severity and outcomes among patients. Median evaluation scores for these symptoms are lower, and the spread of the data implies marked inter-individual heterogeneity. The accompanying heatmap underscores these findings by showing the distribution of symptom evaluations across individual patients. For some symptoms, such as \u0026quot;Epileptic encephalopathy\u0026quot; and \u0026quot;Intellectual disability,\u0026quot; the heatmap reveals extensive variation, with frequent alternations between low (blue) and high (red) evaluation scores across the patient set. This pattern is indicative of diverse clinical presentations, with some patients experiencing severe impairment while others are relatively less affected. More homogeneous patterns, such as that observed for \u0026quot;Normal brain MRI,\u0026quot; confirm the earlier observation from the violin plots. Collectively, these results demonstrate that the burden and manifestations of neurologic symptoms differ greatly within the cohort. Certain neurologic features are relatively spared across most patients, while others show high variability, indicating subgroups within the population with distinct clinical trajectories. This heterogeneity may have implications for prognosis, therapeutic targeting, and individualized care planning.\u003c/p\u003e\n\u003cp\u003e3.2. Clinical spectrum.\u003c/p\u003e\n\u003cp\u003eThe analysis revealed significant relationships between developmental regression (DEV_REG), developmental delayed (DELDEV), and intellectual disability (ID_MI-SEV), which frequently co-occurred, pointing to shared underlying mechanisms likely rooted in disrupted synaptic signaling. Additionally, behavioral manifestations (BEH_MAN) showed a strong association with intellectual disability, further emphasizing the multifaceted and interconnected impact of \u003cem\u003eSYNGAP1\u003c/em\u003e variants on neurodevelopmental and behavioral outcomes.\u003c/p\u003e\n\u003cp\u003e3.3. Patient ordination and symptom contribution.\u003c/p\u003e\n\u003cp\u003eVariants differentiated into three main types as presented in Vlaskamp et al. (2019): (1) truncating variants, which included nonsense and frameshift variants; (2) splice-site variants; (3) missense and in-frame insertion/deletion variants. Splice-site changes, depending whether they affected canonical acceptor-donor sites or noncanonical with possible predicted missense effect were analyzed combined with truncating as loss-of-function (LOF) or missense as beside also analyzing them as a distinct category due to the variety of their possible effects on protein function.\u003c/p\u003e\n\u003cp\u003eNon-metric multidimensional scaling (NMDS) analysis was performed to explore the variation in symptom score profiles among patients harboring different variant types based on the genetic variants identified in the \u003cem\u003eSYNGAP1\u003c/em\u003e gene. Two distinct grouping strategies were applied to classify patients based on their symptom scores. In the first classification scheme (Figure 4A), patients segregated into three variant categories: truncating, missense, and splicing variants. The NMDS plot revealed partial overlap among groups but demonstrated noticeable separation between missense and splicing variant carriers. This distinction was confirmed by Permutational Multivariate Analysis of Variance (PERMANOVA), which identified a statistically significant difference between missense and splicing groups (p \u0026lt; 0.05). These findings suggest that missense and splicing variants are associated with distinct symptom score profiles, reflecting potential differences in their underlying molecular or clinical phenotypes. In the second grouping strategy (Figure 4B), patients were classified into missense and loss-of-function variant groups. NMDS analysis illustrated clear separation between these two clusters, with minimal overlap. PERMANOVA confirmed that the differences between missense and loss-of-function groups were statistically significant (p \u0026lt; 0.05). This result indicates that loss-of-function and missense variants confer distinct symptomatology patterns within the cohort.\u003c/p\u003e\n\u003cp\u003eTogether, these analyses highlight the heterogeneity of symptom manifestations according to variant type. The NMDS coordinates represent underlying multidimensional relationships in patient data, with clustering patterns that point toward variant-specific clinical subtypes. This variant-dependent phenotypic diversity may have important implications for disease prognosis, stratification, and tailored therapeutic approaches.\u003c/p\u003e\n\u003cp\u003e3.4. Response to antiepileptic treatment.\u003c/p\u003e\n\u003cp\u003eIn Figure 5A, patients classified under the second grouping strategy were divided into Split (blue polygon) and Truncate (green polygon) variant groups. The radar plot reveals distinct mean symptom profiles between the two classes. Notably, symptoms such as Developmental regression, Intellectual disability, and EEG abnormalities show varying degrees of severity between the groups, with the Split variant group generally demonstrating higher evaluation scores for several symptoms compared to the Truncate group. This suggests differential clinical impact of these variant types on neurological manifestations.\u003c/p\u003e\n\u003cp\u003eFigure 5B \u0026nbsp;shows a comparison between Missense (orange polygon) and Loss-of-Function (blue polygon) variant groups based on the fourth grouping strategy. The mean symptom evaluation profiles again differ across multiple domains, with Missense variants tending to exhibit relatively elevated scores on some symptom axes, including seizures and behavioral manifestations, relative to Loss-of-Function variants. Conversely, Loss-of-Function variants show higher severity in other symptoms, indicating heterogeneous effects of these variant categories on the neurological phenotype.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of therapeutic approaches in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders revealed distinct patterns in the management of epilepsy, reflecting the diverse severity of symptoms and the complexity of treatment strategies. Polytherapy, in line with expectations, was notably prevalent among patients with severe seizures, particularly those characterized by frequent loss of consciousness or epileptic encephalopathy. This finding underscores the clinical challenge in managing epilepsy in \u003cem\u003eSYNGAP1\u003c/em\u003e variant carriers, as these severe cases often exhibit resistance to standard monotherapy. The high prevalence of polytherapy suggests that a single drug is insufficient to control seizures effectively in this subgroup, necessitating a combination of antiepileptic medications to target multiple pathways involved in seizure activity. However, this approach introduces additional challenges, including an increased risk of drug interactions, cumulative side effects, and a potential impact on overall quality of life. Such cases highlight the critical need for precise diagnostic tools and tailored therapeutic strategies that consider the unique genetic and phenotypic profiles of these patients.\u003c/p\u003e\n\u003cp\u003eConversely, single-drug treatments were observed to be more effective in patients with milder EEG abnormalities, aligning with prior recommendations in the literature. These cases likely represent a less severe disruption of synaptic signaling pathways, allowing for better seizure control with targeted monotherapy. This finding supports the hypothesis that patients with milder variants or partial SynGAP protein functionality may respond well to a focused therapeutic approach. Importantly, this aligns with the growing emphasis on individualized medicine in epilepsy management, where treatment regimens are tailored not only to symptom severity but also to the underlying genetic etiology.\u003c/p\u003e\n\u003cp\u003eThe distinction between polytherapy and monotherapy efficacy highlights the need for stratified treatment protocols in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders. Patients with severe phenotypes, particularly those exhibiting EEG abnormalities consistent with generalized or multifocal seizure activity, may benefit from early and aggressive intervention, potentially incorporating novel therapeutic approaches such as gene therapy or targeted pharmacological modulation of the Ras/ERK signaling pathway. In contrast, those with milder EEG profiles might achieve optimal outcomes with a conservative, single-drug regimen, reducing the burden of side effects and improving adherence.\u003c/p\u003e\n\u003cp\u003eAdditionally, the observed patterns of treatment efficacy emphasize the importance of regular monitoring and re-evaluation of therapeutic strategies in these patients. As new pharmacological options and genetic therapies become available, a dynamic approach to treatment adjustment could further enhance seizure control and quality of life. This analysis underscores the complexity of managing epilepsy in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders, highlighting the interplay between genetic, clinical, and therapeutic factors that dictate treatment success. These findings advocate for a multidisciplinary approach, integrating genetic diagnostics, neurologic assessment, and pharmacological expertise to optimize outcomes for this diverse patient population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Correlation with Clinical Scoring System\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented in Summary Table: Variant Class and Major Clinical Scores (Table 3)\u003c/p\u003e\n\u003cp\u003e4.1. Intellectual disability and developmental delay\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eSeverity (Scoring: 1\u0026ndash;3): Uniformly moderate to severe intellectual disability was observed, regardless of variant class (protein-truncating, splice-site, missense). Scores for most patients clustered at the higher end.\u003c/li\u003e\n \u003cli\u003eDomain effect: Certain reports (including analysis in large international cohorts) propose that variants in exons 1\u0026ndash;6 may correlate with milder intellectual disability; however, this pattern was not clearly validated within the scoring profile of this specific Polish cohort\u003csup\u003e[1]\u003c/sup\u003e\u003csup\u003e[2]\u003c/sup\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e4.2 Epilepsy\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003ePrevalence and morphological type (Scoring: detailed by type): Nearly all patients, independent of variant, scored positively for epilepsy, with high rates of generalized, absence, and myoclonic seizures.\u003c/li\u003e\n \u003cli\u003eResponse to therapy: Some literature suggests that variants in exons 4\u0026ndash;5 may be associated with more pharmacoresponsive epilepsy, while those in exons 8\u0026ndash;15 could be linked to more refractory forms (Li et al., 2023). In this cohort\u0026rsquo;s scoring matrix, seizure severity and medication counts did not show stratified distribution by variant class.\u003c/li\u003e\n \u003cli\u003eEEG and encephalopathy: Abnormal EEG and epileptic encephalopathy were common across variant types, with no strong inter-group differences in scoring.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e4.3 Behavioral and ASD features\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eASD/Behavioral phenotype (Scoring 0\u0026ndash;3): All classes of \u003cem\u003eSYNGAP1\u003c/em\u003e variants led to similar high-scoring profiles for autistic traits, stereotypies, and behavioral disturbances.\u003c/li\u003e\n \u003cli\u003eDomain trend: A potential trend toward reduced ASD risk with variants in the distal part of the gene (exons 1\u0026ndash;6) has been proposed in larger datasets, but this effect, if present, was not robust within the present structured scoring results\u003csup\u003e[1]\u003c/sup\u003e\u003csup\u003e[2]\u003c/sup\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e4.4 Additional clinical domains\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMotor and Hypotonia: Scoring for hypotonia, gait disturbance, and microcephaly did not segregate by variant type.\u003c/li\u003e\n \u003cli\u003eMRI Findings: Minor anatomical brain changes were observed in some patients but did not correlate with specific genetic classes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo genotype\u0026ndash;scoring stratification emerged for domain or variant class, with minimal effect of variant position on system scoring profiles.\u003c/p\u003e\n\u003cp\u003e4.5 General Clinical Correlates\u003c/p\u003e\n\u003cp\u003eConsistent with global studies, all children with \u003cem\u003eSYNGAP1\u003c/em\u003e P/LP variants presented with:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGlobal developmental delay and moderate to severe intellectual disability\u003c/li\u003e\n \u003cli\u003eHigh rates of epilepsy (mainly generalized and myoclonic forms)\u003c/li\u003e\n \u003cli\u003eFrequent autistic traits, abnormal behaviors, and speech impairment\u003c/li\u003e\n \u003cli\u003eAdditional findings: microcephaly, gait and coordination problems, and feeding difficulties.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo clear genotype\u0026ndash;phenotype correlations with specific variant classes were apparent, echoing recent registry-based findings (Wiltrout et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe detected spectrum of \u003cem\u003eSYNGAP1\u003c/em\u003e variants in this cohort mirrors the global mutational landscape for MRD5. Most are \u003cem\u003ede novo\u003c/em\u003e, protein-disrupting changes with conclusive evidence for pathogenicity, underlying a consistent and severe neurodevelopmental syndrome (Li et al., 2023; Berryer et al., 2013; Mayo Clinic, 2013; Wiltrout et al., 2024; Meili et al., 2021).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis cohort analysis of Polish patients with \u003cem\u003eSYNGAP1\u003c/em\u003e-related neurodevelopmental disorders comprised 30 unrelated individuals (50% females; median age at diagnosis 4.8 years) in whom a spectrum of pathogenic variants was identified. The variant type distribution demonstrated a predominance of loss-of-function variants: truncating variants (nonsense and frameshift) occurred in 18 patients (60.0%), missense variants in 6 patients (20.0%), splice-site variants in 5 patients (16.7%), and in-frame deletions in 1 patient (3.3%). All 30 variants (100%) were confirmed as \u003cem\u003ede novo\u003c/em\u003e through parental testing, confirming the sporadic nature of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders. The age distribution at diagnosis showed that the majority of cases were recognized in early childhood (2-5 years): 14 patients (46.7%), school age (5-10 years): 9 patients (30.0%), adolescence (\u0026gt;10 years): 5 patients (16.7%), and infancy (0-2 years): 2 patients (6.7%). This cohort reflects the characteristic patterns of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders with a predominance of loss-of-function variants (60.0%), significant representation of splicing defects (16.7%), and universal \u003cem\u003ede novo\u003c/em\u003e inheritance, which remains consistent with the haploinsufficiency model, where truncating variants were associated with the most severe phenotypes, splice-site variants showed the broadest phenotypic spectrum, and missense variants generally caused milder clinical manifestations.\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eGenotype-phenotype variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study identified novel \u003cem\u003eSYNGAP1\u003c/em\u003e variants alongside previously reported variants, providing a comprehensive landscape of the gene\u0026apos;s pathogenic profile. Truncating variants, particularly nonsense and frameshift variants, were strongly associated with severe intellectual disability and developmental regression, including developmental and epileptic encephalopathy. These observations are consistent with published reports demonstrating that truncating variants predominantly lead to haploinsufficiency and result in significant phenotypic severity (Mignot et al., 2016; Vlaskamp et al., 2019). The loss-of-function mechanism underlying these variants impairs synaptic signalling pathways critical for neurodevelopment (Hamdan et al., 2009).\u003c/p\u003e\n\u003cp\u003eInterestingly, the milder and more variable symptomatology seen in some patients harbouring missense variants aligns with prior studies suggesting that partial preservation of protein function may mitigate clinical outcomes (Carvill et al., 2013; Meili et al., 2021). However, in our cohort, behavioral phenotypes clustered distinctly in certain missense cases, diverging from many prior reports that primarily emphasized motor and cognitive delays. This divergence implies that additional factors, such as genetic modifiers or environmental influences, may modulate phenotypic expression in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders, a hypothesis supported by recent investigations into phenotypic variability in neurodevelopmental conditions (Yang et al. 2021).\u003c/p\u003e\n\u003cp\u003ePathogenicity in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders is principally caused by haploinsufficiency, with 80% of patients in our cohort harboring loss-of-function (LoF) variants (including 60% truncating variants, 16.7% splice-site variants, and 3.3% complex variants). Nevertheless, symptomatic patients with missense variants (20% of our cohort), often classified as variants of uncertain significance (VUS), are increasingly being interpreted as pathogenic when comprehensive functional and clinical evidence accumulates (Jimenez-Gomez et al., 2019). Missense variants in \u003cem\u003eSYNGAP1\u003c/em\u003e represent a mechanistically heterogeneous class, with pathogenic consequences extending beyond simple haploinsufficiency. These effects may include hypermorphic (gain-of-function), neomorphic (novel-function), or dominant-negative impacts, each capable of uniquely disrupting synaptic signaling pathways (Weldon et al., 2018; Mignot et al., 2016). This functional heterogeneity emphasizes the critical importance of robust genotype\u0026ndash;phenotype correlations in clinical interpretation and motivates broad, detailed assessments of patient cohorts to capture the full spectrum of \u003cem\u003eSYNGAP1\u003c/em\u003e-related clinical presentations\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eNeurological symptoms and clinical management\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe heatmaps and violin plots presented in our results emphasize the high prevalence of seizures and EEG abnormalities across all \u003cem\u003eSYNGAP1\u003c/em\u003e variant types, reinforcing prior research that defines epilepsy as a core clinical feature of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders (Mignot et al., 2016). Our findings also highlight a pronounced co-occurrence of developmental regression and epilepsy, indicative of developmental and epileptic encephalopathy, a phenomenon supported by the critical role of SynGAP in synaptic plasticity and neuronal signaling pathways (Vlaskamp et al., 2019; Clement et al., 2012). These shared mechanistic underpinnings suggest that disruptions in SynGAP function perturb synaptic development and excitability, leading to neurodevelopmental deterioration alongside epileptic activity.\u003c/p\u003e\n\u003cp\u003eCompared to previous studies focusing predominantly on Western European or North American populations, our cohort exhibited a somewhat higher prevalence of polytherapy-resistant seizures. This increased seizure treatment refractoriness may reflect regional differences that could stem from genetic background diversity, environmental influences, or disparities in healthcare access and management protocols (Carvill et al., 2013). These findings underscore the need for international collaborative research efforts to elucidate factors underpinning treatment variability and optimize individualized therapeutic strategies for \u003cem\u003eSYNGAP1\u003c/em\u003e-related epilepsy\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eAdvanced analytical insights.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-metric multidimensional scaling (NMDS) analysis conducted in our study provided robust evidence of distinct symptom clustering related to \u003cem\u003eSYNGAP1\u003c/em\u003e variant types. Specifically, patients carrying truncating variants exhibited the most severe clinical phenotype, characterized by profound intellectual disability and frequent epileptic encephalopathy. Conversely, patients with missense variants demonstrated a broader phenotypic spectrum, including cases presenting with milder developmental delays and variable neurological impairment. These results corroborate earlier findings, which similarly delineated genotype-dependent phenotypic variability in \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders (Vlaskamp et al., 2019; Mignot et al., 2016). Importantly, our study advances the field by applying Similarity Percentage (SIMPER) analysis to quantify the individual symptom contributions driving group differences, highlighting severe intellectual disability, seizures, and developmental regression as key discriminators.\u003c/p\u003e\n\u003cp\u003eThe analytical framework employed not only reaffirms well-established genotype-phenotype correlations but also offers a scalable and replicable model for patient stratification in clinical trial design. By stratifying patients into biologically and clinically homogeneous subgroups based on detailed symptom profiles and underlying genetic variants, this approach supports more precise therapeutic targeting and enhances the potential for personalized medicine (Jimenez-Gomez et al., 2019; Holder et al., 2018). As targeted therapies for \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders, including gene replacement and pathway modulation strategies, continue to develop, such stratification methods will be critical to optimize patient selection and maximize clinical trial efficacy\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eComparison with literature on therapeutic implications.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe therapeutic landscape for \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders remains in its early stages, with current clinical management predominantly focused on symptomatic treatment. Antiepileptic drugs remain the cornerstone for controlling seizures, complemented by supportive therapies targeting cognitive and behavioral impairments (Vissers et al., 2023). Our study adds to this growing body of evidence by underscoring the considerable variability in treatment response, particularly highlighting that polytherapy is often necessary for patients exhibiting EEG abnormalities and epileptic encephalopathy, reflecting the complexity and severity of seizure phenotypes in \u003cem\u003eSYNGAP1\u003c/em\u003e-related conditions (Jimenez-Gomez et al., 2023; Wiltrout et al., 2024). This variability aligns with prior observations of drug-resistant epilepsy in this population, posing significant clinical challenges (Carvill et al., 2013).\u003c/p\u003e\n\u003cp\u003eEmerging therapeutic strategies hold promises for addressing the underlying molecular pathology of \u003cem\u003eSYNGAP1\u003c/em\u003e disorders. Gene therapy approaches aimed at restoring \u003cem\u003eSYNGAP1\u003c/em\u003e expression or compensating for haploinsufficiency are under active investigation and show encouraging preclinical results (Thomas et al., 2021). Additionally, pharmacological modulation of downstream effectors such as the Ras/ERK signaling pathway represents a promising avenue to rectify synaptic dysfunction directly (Mignot et al., 2016; Clement et al., 2012). Notably, our identification of specific genetic variant clusters within the cohort offers a strategic framework for patient prioritization in these experimental interventions. For instance, carriers of truncating variants who experience severe developmental regression might be ideal candidates for gene replacement therapies, whereas patients harboring missense variants could derive greater benefit from pathway-specific pharmacological modulation (Michaelson et al., 2018; Jimenez-Gomez et al., 2023). This precision medicine approach underlines the critical importance of integrating comprehensive genotype-phenotype data to optimize therapeutic outcomes.\u003c/p\u003e\n\u003cp\u003e5\u003cstrong\u003e. Limitations and recommendations\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eWhile our findings align with and extend existing literature, several limitations merit careful consideration. First, the relatively small sample size and regional focus on a Polish pediatric cohort may limit the generalizability of our results to broader, more diverse populations. Expanding future studies to include multiethnic and international cohorts could uncover additional genotype-phenotype correlations and reveal modifiers such as environmental exposures, lifestyle factors, or epigenetic influences that may affect disease expression (Karaca et al., 2018; McRae et al., 2017). Such broader studies are critical to elucidate the heterogeneity and penetrance of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders across different populations.\u003c/p\u003e\n\u003cp\u003eMoreover, an important limitation of our study is the reliance on parent-reported clinical data collected through questionnaires, which introduces the potential for subjective bias and recall inaccuracies. This may impact the precision of symptom characterization, particularly for complex behavioral and developmental features (Newman et al., 2016). Future longitudinal research employing comprehensive, standardized clinical assessments, coupled with advanced neuroimaging modalities and molecular biomarker analyses, will provide more objective, granular insights into the natural history and progression of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders (Eising et al., 2019; Jensen \u0026amp; Bonde, 2020). Integrating multi-omics approaches and functional studies will also enhance our understanding of the underlying pathophysiological mechanisms and inform tailored therapeutic strategies.\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eFuture directions\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eOur study underscores the critical role of \u003cem\u003eSYNGAP1\u003c/em\u003e in neurodevelopment, emphasizing the gene\u0026rsquo;s diverse and severe phenotypic consequences spanning intellectual disability, epilepsy, and autism spectrum features. By integrating comprehensive genetic analyses with advanced phenotype quantification and multivariate methods, we provide a robust framework for elucidating the complex interplay between genotype and clinical presentation. This approach not only reveals complex genotype-phenotype correlations, with both truncating and missense variants demonstrating broad phenotypic variability, but also introduces novel insights specific to our Polish cohort, including detailed symptom contribution profiles and nuanced phenotypic heterogeneity that highlights the need for comprehensive functional studies and identification of phenotype-modifying factors beyond variant type alone (Vlaskamp et al., 2019; Jimenez-Gomez et al., 2023).\u003c/p\u003e\n\u003cp\u003eLooking ahead, the expansion of research through international collaborations and larger, more ethnically diverse cohorts will be pivotal in uncovering additional genotype-phenotype relationships and potential disease modifiers, including environmental and epigenetic factors (Karaca et al., 2018; McRae et al., 2017). Furthermore, the integration of cutting-edge therapeutic modalities, such as gene replacement therapies and targeted pharmacological interventions modulating the Ras/ERK pathway, holds significant promise. Such advances will require precise patient stratification informed by comprehensive molecular and clinical profiling to optimize treatment efficacy (Thomas et al., 2021; Mignot et al., 2016). The translational pathway from bench to bedside remains challenging due to the complexity of \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders, but ongoing progress in genomic medicine and therapeutic development offers renewed hope for more precise and effective interventions in the near future (Clement et al., 2012; Holder et al., 2018).\u003c/p\u003e\n\u003cp\u003eThis study highlights the indispensable role of detailed molecular characterization in enhancing diagnostic precision and guiding personalized therapeutic decisions for \u003cem\u003eSYNGAP1\u003c/em\u003e-related conditions. By leveraging genotype-first approaches and sophisticated clinical analytics, our work lays a foundational platform for future research endeavors. These efforts are essential to realize the potential of precision medicine in neurodevelopmental disorders, ultimately improving outcomes for affected individuals worldwide.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this well-characterized \u003cem\u003eSYNGAP1\u003c/em\u003e cohort of 30 Polish pediatric patients, our comprehensive scoring system revealed complex genotype-phenotype relationships that challenge simple correlations between variant type and clinical severity. While our analysis confirmed uniformly significant phenotypic impact across all pathogenic variant classes, substantial phenotypic heterogeneity was observed within both truncating and missense variant groups, indicating that factors beyond variant classification influence disease manifestation.\u003c/p\u003e\u003cp\u003eNotably, truncating variants demonstrated considerable phenotypic variability rather than uniform severe outcomes. For example, patient 22 with variant c.155C\u0026thinsp;\u0026gt;\u0026thinsp;A p.Ser52Ter presented with a relatively mild phenotype\u0026mdash;no seizures, no developmental regression, excluded autism diagnosis, and intellectual disability ranging from moderate to average intelligence. Conversely, patient 24 with c.2993delC p.Pro978Hisfs*99 exhibited severe manifestations including profound intellectual disability and confirmed autism spectrum disorder. This variability within the same variant class suggests that additional genetic modifiers, environmental factors, or epigenetic influences significantly contribute to phenotypic expression.\u003c/p\u003e\u003cp\u003eSimilarly, missense variants showed broad phenotypic ranges, with some patients presenting severe symptoms comparable to truncating variants, while others demonstrated relatively milder presentations. The absence of statistically significant stratification by variant domain, chromosomal position, or specific variant class underscores the complex pathophysiology underlying \u003cem\u003eSYNGAP1\u003c/em\u003e-related disorders that extends beyond simple haploinsufficiency models.\u003c/p\u003e\u003cp\u003eOur findings align with emerging evidence that \u003cem\u003eSYNGAP1\u003c/em\u003e pathogenicity involves multifaceted mechanisms including haploinsufficiency, potential dominant-negative effects, and disruption of critical synaptic plasticity pathways, with clinical severity influenced by multiple genetic and non-genetic factors rather than variant type alone3. This phenotypic complexity necessitates individualized clinical management approaches and highlights the importance of comprehensive functional studies to elucidate the full spectrum of \u003cem\u003eSYNGAP1\u003c/em\u003e pathogenic mechanisms.\u003c/p\u003e\u003cp\u003eThe uniformly significant developmental and neurological impairment observed across all variant categories, combined with the shared presence of core features such as seizures, hypotonia, and behavioral symptoms, supports \u003cem\u003eSYNGAP1\u003c/em\u003e dysfunction as the primary pathogenic driver, while emphasizing that variant classification alone is insufficient to predict individual patient outcomes or inform precision medicine approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNo funding.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eConflict of interest: The authors declare no competing interests.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eE-mails\u003c/span\u003e:\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTomasz Skalski: [email protected]\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eKarolina Chwialkowska: [email protected]\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors wrote the main manuscript text, reviewed and accepted the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAceti M, Creson TK, Vaissiere T, Rojas C, Huang WC, Wang YX et al (2015) Syngap1 haploinsufficiency damages a postnatal critical period of pyramidal cell structural maturation linked to cortical circuit assembly. Biol Open 4(6):659\u0026ndash;668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1242/bio.20148418\u003c/span\u003e\u003cspan address=\"10.1242/bio.20148418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26(1):32\u0026ndash;46\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAraki Y, Zeng M, Zhang M, Huganir RL (2020) Structural basis of synaptic Ras GTPase-activating protein function in learning and memory. Nat Commun 11:4392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-18104-5\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-18104-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerryer MH, Hamdan FF, Klitten LL et al (2013) Mutations in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum Mutat 34(2):385\u0026ndash;393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/humu.22248\u003c/span\u003e\u003cspan address=\"10.1002/humu.22248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarvill GL, Regan BM, Yendle SC et al (2013) Mutations in the GTPase-activating protein SynGAP cause intellectual disability and epilepsy. Am J Hum Genet 92(1):89\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2012.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2012.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18(1):117\u0026ndash;143\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClement JP, Aceti M, Creson TK, Ozkan ED, Shi Y, Reish NJ et al (2012) Pathogenic SYNGAP1 mutations impair cognitive development by disrupting maturation of dendritic spine synapses. Cell 151(4):709\u0026ndash;723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2012.08.045\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.08.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEising E, Carrion-Castillo A, Vino A et al (2019) A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Neuroimage Clin 23:101842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nicl.2019.101842\u003c/span\u003e\u003cspan address=\"10.1016/j.nicl.2019.101842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGamache TR, Kong E, Kaczorowski CC (2023) Recent advances in understanding SYNGAP1-related neurodevelopmental disorders. Neurobiol Dis 171:105123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2022.105123\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2022.105123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo X, Chen L, Yin J, Wang T, Huang G (2021) Molecular mechanisms regulating the Ras/ERK signaling pathway in synaptic plasticity: role of SynGAP. Neurosci Lett 739:135400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neulet.2020.135400\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2020.135400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamdan FF, Gauthier J, Spiegelman D et al (2009) Mutations in SYNGAP1 in autosomal nonsyndromic mental retardation. N Engl J Med 360:599\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa0805392\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa0805392\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamdan FF, Daoud H, Piton A et al (2011) De novo SYNGAP1 mutations in nonsyndromic intellectual disability and autism. Biol Psychiatry 69(9):898\u0026ndash;901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biopsych.2010.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2010.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison SM, Biesecker LG, Rehm HL (2023) ClinGen expert clinical validity curation of 518 gene\u0026ndash;disease pairs. Genet Med 25(4):795\u0026ndash;804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gim.2022.12.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gim.2022.12.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolder JL Jr et al (2018) Precision medicine for genetic epilepsies: integrating genomics into clinical care. Neurotherapeutics 15(4):993\u0026ndash;1001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13311-018-00679-z\u003c/span\u003e\u003cspan address=\"10.1007/s13311-018-00679-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIoannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877\u0026ndash;885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2016.08.016\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2016.08.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvenshitz M, Segal M (2010) Neuregulin-1 induces expression of α-actinin and alters dendritic spine morphology in hippocampal neurons via Ras/ERK pathway. J Neurochem 113(4):862\u0026ndash;872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-4159.2010.06657.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-4159.2010.06657.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaganathan K, Panagiotopoulou G, McRae JF et al (2019) Predicting splicing from primary sequence with deep learning. Cell 176(3):535\u0026ndash;548e24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2018.12.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2018.12.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJensen FE, Bonde L (2020) Integrating molecular and imaging modalities in understanding epilepsy. Epilepsy Res 163:106333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eplepsyres.2020.106333\u003c/span\u003e\u003cspan address=\"10.1016/j.eplepsyres.2020.106333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimenez-Gomez A, Nair DR, Myrick LK, Holder JL Jr et al (2019) SYNGAP1 haploinsufficiency and neurodevelopmental disorders: emerging clinical features. Epilepsy Res 149:31\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eplepsyres.2018.11.009\u003c/span\u003e\u003cspan address=\"10.1016/j.eplepsyres.2018.11.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimenez-Gomez A, Holder JL Jr et al (2023) Comprehensive phenotypic analysis of SYNGAP1-related disorders suggests differential effects of variant type. Genet Med 25(4):100006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gim.2023.100006\u003c/span\u003e\u003cspan address=\"10.1016/j.gim.2023.100006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarczewski KJ, Francioli LC, Tiao G et al (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581(7809):434\u0026ndash;443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-2308-7\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2308-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaraca E, Harel T, Pehlivan D et al (2018) Genes that affect brain structure and function identified by rare variant analyses of Mendelian neurodevelopmental disorders. Am J Hum Genet 102(4):695\u0026ndash;712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2018.03.014\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2018.03.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKazdoba TM, Leach PT, Crawley JN (2016) Behavioral phenotypes of genetic mouse models of autism. Genes Brain Behav 15(1):7\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gbb.12256\u003c/span\u003e\u003cspan address=\"10.1111/gbb.12256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKilinc M, G\u0026ouml;k\u0026ccedil;e O, Rumbaugh G (2018) The synaptic pathology of autism spectrum disorders. Nat Rev Neurosci 19:275\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn.2018.10\u003c/span\u003e\u003cspan address=\"10.1038/nrn.2018.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKilinc M et al (2021) Species-conserved SYNGAP1 phenotypes. Brain 144(7):2140\u0026ndash;2150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/awab130\u003c/span\u003e\u003cspan address=\"10.1093/brain/awab130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKomiyama NH, Watabe AM, Carlisle HJ, Porter K, Charlesworth P, Monti J et al (2002) SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the hippocampus. Neuron 35(6):903\u0026ndash;914. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0896-6273(02)00857-5\u003c/span\u003e\u003cspan address=\"10.1016/S0896-6273(02)00857-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi B, Sun P, Gao Z, Huang L, Zhang Z, Zhang H (2023) Identification and functional characterization of de novo SYNGAP1 variant causing intellectual disability. Front Genet 14:1270175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2023.1270175\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2023.1270175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcRae JF, Clayton S, Fitzgerald TW et al (2017) Prevalence and architecture of de novo mutations in developmental disorders. Nature 542(7642):433\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature21062\u003c/span\u003e\u003cspan address=\"10.1038/nature21062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeili F et al (2021) Multi-parametric analysis of 57 SYNGAP1 variants reveals functional diversity across variants and assays. Epilepsy Res 174:106650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eplepsyres.2021.106650\u003c/span\u003e\u003cspan address=\"10.1016/j.eplepsyres.2021.106650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichaelson JJ, Shi Y, Gujral M, Zheng H, Malhotra D, Jin X et al (2018) Functional disruption of SYNGAP1 causes synaptic signaling defects and autism-associated phenotypes. Cell 173(3):775\u0026ndash;789e17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2018.02.052\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2018.02.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMignot C, von St\u0026uuml;lpnagel C, Nava C, Ville D, Sanlaville D, Lesca G et al (2016) Genetic and neurodevelopmental spectrum of SYNGAP1-associated intellectual disability and epilepsy. J Med Genet 53(8):511\u0026ndash;522. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jmedgenet-2015-103451\u003c/span\u003e\u003cspan address=\"10.1136/jmedgenet-2015-103451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewman S, Hermetz KE, Weckselblatt B, Rudd MK (2016) Next-generation sequencing of duplication CNVs reveals that most are tandem and some create fusion genes at breakpoints. Am J Hum Genet 98(5):967\u0026ndash;983. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2016.03.016\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2016.03.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOksanen J, Blanchet FG, Friendly M et al (2020) vegan: Community Ecology Package. R package version 2.5-7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzkan ED, Creson TK, Kram\u0026aacute;r EA, Rojas C, Seese RR, Babyan AH et al (2014) Reduced cognition in SYNGAP1 mutants is caused by isolated damage within developing forebrain excitatory neurons. Neuron 82(6):1317\u0026ndash;1333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2014.04.048\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2014.04.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzkan ED, Creson TK et al (2015) Reduced cognition in Syngap1 mutants. J Neurosci 35(47):15834\u0026ndash;15842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.0974-15.2015\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.0974-15.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePenn AC, Zhang CL, Georges F et al (2017) Hippocampal LTP is enhanced by inhibiting the Ras/ERK pathway via SynGAP modulation. Mol Ther 25(6):1340\u0026ndash;1349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymthe.2017.03.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ymthe.2017.03.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants. Genet Med 17(5):405\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/gim.2015.30\u003c/span\u003e\u003cspan address=\"10.1038/gim.2015.30\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRumbaugh G, Adams JP, Kim JH, Huganir RL (2006) SynGAP regulates synaptic strength and mitogen-activated protein kinases in cultured neurons. Proc Natl Acad Sci U S A 103(12):4344\u0026ndash;4349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0600084103\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0600084103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u0026ndash;2504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.1239303\u003c/span\u003e\u003cspan address=\"10.1101/gr.1239303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrivastava S, Cohen JS, Vernon H et al (2014) Clinical whole exome sequencing in child neurology practice. Ann Neurol 76(4):473\u0026ndash;483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ana.24251\u003c/span\u003e\u003cspan address=\"10.1002/ana.24251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas RH et al (2021) Preclinical development of gene therapy for SYNGAP1 haploinsufficiency. Mol Ther 29(3):1326\u0026ndash;1339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymthe.2020.12.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ymthe.2020.12.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVissers LELM, Holder JL Jr, Jimenez-Gomez A et al (2023) Supportive and symptomatic therapies in SYNGAP1-related neurodevelopmental disorders. Neurotherapeutics 20(3):812\u0026ndash;829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13311-023-01379-z\u003c/span\u003e\u003cspan address=\"10.1007/s13311-023-01379-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVlaskamp DRM, Callenbach PMC, Rump P, Fișeșan C, Bahi-Buisson N, den Hollander NS et al (2019) SYNGAP1 encephalopathy: a distinctive generalized developmental and epileptic encephalopathy. Neurology 92(2):e96\u0026ndash;e107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000006729\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000006729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeldon M, Kilinc M, Holder JL Jr et al (2018) Clinical variability in SYNGAP1-related intellectual disability and epilepsy. Epilepsia 59(5):e56\u0026ndash;e63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/epi.14046\u003c/span\u003e\u003cspan address=\"10.1111/epi.14046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiltrout K, Holder JL Jr et al (2024) Comprehensive phenotypes of patients with SYNGAP1-related disorder reveal high rates of epilepsy and autism. Epilepsia 65:1428\u0026ndash;1438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/epi.17913\u003c/span\u003e\u003cspan address=\"10.1111/epi.17913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Medical Association (2013) Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191\u0026ndash;2194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2013.281053\u003c/span\u003e\u003cspan address=\"10.1001/jama.2013.281053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang X, Wang L, Zhou Y et al (2021) Genetic modifiers and phenotypic variability in neurodevelopmental disorders: insights from clinical cohorts. Front Neurosci 15:642563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2021.642563\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2021.642563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng T, Li YI (2022) Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol 23:103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-022-02664-4\u003c/span\u003e\u003cspan address=\"10.1186/s13059-022-02664-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2021) Longitudinal neuroimaging in SYNGAP1-related disorders reveals disrupted connectivity. Brain 144(12):3570\u0026ndash;3585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/awab285\u003c/span\u003e\u003cspan address=\"10.1093/brain/awab285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGamache TR, Kong E, Kaczorowski CC (2023) Recent advances in understanding SYNGAP1-related neurodevelopmental disorders. Neurobiol Dis 171:105123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2022.105123\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2022.105123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClement JP, Aceti M, Creson TK, Ozkan ED, Shi Y, Reish NJ et al (2012) Pathogenic SYNGAP1 mutations impair cognitive development by disrupting maturation of dendritic spine synapses. Cell 151(4):709\u0026ndash;723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2012.08.045\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.08.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAraki Y, Zeng M, Zhang M, Huganir RL (2020) Structural basis of synaptic Ras GTPase-activating protein function in learning and memory. Nat Commun 11:4392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-18104-5\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-18104-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzkan ED, Creson TK, Kram\u0026aacute;r EA, Rojas C, Seese RR, Babyan AH et al (2014) Reduced cognition in SYNGAP1 mutants is caused by isolated damage within developing forebrain excitatory neurons. Neuron 82(6):1317\u0026ndash;1333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2014.04.048\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2014.04.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo X, Chen L, Yin J, Wang T, Huang G (2021) Molecular mechanisms regulating the Ras/ERK signaling pathway in synaptic plasticity: role of SynGAP. Neurosci Lett 739:135400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neulet.2020.135400\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2020.135400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKomiyama NH, Watabe AM, Carlisle HJ, Porter K, Charlesworth P, Monti J et al (2002) SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the hippocampus. Neuron 35(6):903\u0026ndash;914. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0896-6273(02)00857-5\u003c/span\u003e\u003cspan address=\"10.1016/S0896-6273(02)00857-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvenshitz M, Segal M (2010) Neuregulin-1 induces expression of α-actinin and alters dendritic spine morphology in hippocampal neurons via Ras/ERK pathway. J Neurochem 113(4):862\u0026ndash;872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-4159.2010.06657.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-4159.2010.06657.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRumbaugh G, Adams JP, Kim JH, Huganir RL (2006) SynGAP regulates synaptic strength and mitogen-activated protein kinases in cultured neurons. Proc Natl Acad Sci U S A 103(12):4344\u0026ndash;4349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0600084103\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0600084103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVlaskamp DRM, Callenbach PMC, Rump P, Fișeșan C, Bahi-Buisson N, den Hollander NS et al (2019) SYNGAP1 encephalopathy: a distinctive generalized developmental and epileptic encephalopathy. Neurology 92(2):e96\u0026ndash;e107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000006729\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000006729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMignot C, von St\u0026uuml;lpnagel C, Nava C, Ville D, Sanlaville D, Lesca G et al (2016) Genetic and neurodevelopmental spectrum of SYNGAP1-associated intellectual disability and epilepsy. J Med Genet 53(8):511\u0026ndash;522. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jmedgenet-2015-103451\u003c/span\u003e\u003cspan address=\"10.1136/jmedgenet-2015-103451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKilinc M, G\u0026ouml;k\u0026ccedil;e O, Rumbaugh G (2018) The synaptic pathology of autism spectrum disorders. Nat Rev Neurosci 19:275\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn.2018.10\u003c/span\u003e\u003cspan address=\"10.1038/nrn.2018.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAceti M, Creson TK, Vaissiere T, Rojas C, Huang WC, Wang YX et al (2015) Syngap1 haploinsufficiency damages a postnatal critical period of pyramidal cell structural maturation linked to cortical circuit assembly. Biol Open 4(6):659\u0026ndash;668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1242/bio.20148418\u003c/span\u003e\u003cspan address=\"10.1242/bio.20148418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerryer MH, Hamdan FF, Klitten LL et al (2013) Mutations in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum Mutat 34(2):385\u0026ndash;393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23161826/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/23161826/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamdan FF, Gauthier J, Spiegelman D et al (2009) Mutations in SYNGAP1 in autosomal nonsyndromic mental retardation. N Engl J Med 360:599\u0026ndash;605\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamdan FF, Daoud H, Piton A et al (2011) De novo SYNGAP1 mutations in nonsyndromic intellectual disability and autism. Biol Psychiatry 69(9):898\u0026ndash;901\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzkan ED, Creson TK et al (2015) Reduced cognition in Syngap1 mutants. J Neurosci 35(47):15834\u0026ndash;15842\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePenn AC, Zhang CL, Georges F et al (2017) Hippocampal LTP is enhanced by inhibiting the Ras/ERK pathway via SynGAP modulation. Mol Ther 25(6):1340\u0026ndash;1349\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKilinc M et al (2021) Species-conserved SYNGAP1 phenotypes. Brain 144(7):2140\u0026ndash;2150\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Medical Association (2013) Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191\u0026ndash;2194\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards S, Aziz N, Bale S et al (2015) Standards and guidelines for the interpretation of sequence variants. Genet Med 17(5):405\u0026ndash;424\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison SM, Biesecker LG, Rehm HL (2023) ClinGen expert clinical validity curation of 518 gene\u0026ndash;disease pairs. Genet Med 25(4):795\u0026ndash;804\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaganathan K, Panagiotopoulou G, McRae JF et al (2019) Predicting splicing from primary sequence with deep learning. Cell 176(3):535\u0026ndash;548e24\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng T, Li YI (2022) Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol 23:103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35449021/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35449021/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIoannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877\u0026ndash;885\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarczewski KJ, Francioli LC, Tiao G et al (2020) The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581(7809):434\u0026ndash;443\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18(1):117\u0026ndash;143\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26(1):32\u0026ndash;46\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOksanen J, Blanchet FG, Friendly M et al (2020) vegan: Community Ecology Package. R package version 2.5-7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u0026ndash;2504\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarvill GL, Regan BM, Yendle SC et al (2013) Mutations in the GTPase-activating protein SynGAP cause intellectual disability and epilepsy. Am J Hum Genet 92(1):89\u0026ndash;103\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimenez-Gomez A, Nair DR, Myrick LK, Holder JL Jr et al (2019) SYNGAP1 haploinsufficiency and neurodevelopmental disorders: emerging clinical features. Epilepsy Res 149:31\u0026ndash;37\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolder JL Jr et al (2018) Precision medicine for genetic epilepsies: integrating genomics into clinical care. Neurotherapeutics 15(4):993\u0026ndash;1001\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVissers LELM, Holder JL Jr, Jimenez-Gomez A et al (2023) Supportive and symptomatic therapies in SYNGAP1-related neurodevelopmental disorders. Neurotherapeutics 20(3):812\u0026ndash;829\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJimenez-Gomez A et al (2023) Haploinsufficiency of SYNGAP1 underlies neurodevelopmental disorders. Genet Med 25(4):795\u0026ndash;804\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiltrout K, Holder JL Jr et al (2024) Comprehensive phenotypes of patients with SYNGAP1-related disorder reveal high rates of epilepsy and autism. Epilepsia 65:1428\u0026ndash;1438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/epi.17913\u003c/span\u003e\u003cspan address=\"10.1111/epi.17913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas RH et al (2021) Preclinical development of gene therapy for SYNGAP1 haploinsufficiency. Mol Ther 29(3):1326\u0026ndash;1339\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichaelson JJ, Shi Y, Gujral M, Zheng H, Malhotra D, Jin X et al (2018) Functional disruption of SYNGAP1 causes synaptic signaling defects and autism-associated phenotypes. Cell 173(3):775\u0026ndash;789e17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaraca E, Harel T, Pehlivan D et al (2018) Genes that affect brain structure and function identified by rare variant analyses of Mendelian neurodevelopmental disorders. Am J Hum Genet 102(4):695\u0026ndash;712\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcRae JF, Clayton S, Fitzgerald TW et al (2017) Prevalence and architecture of de novo mutations in developmental disorders. Nature 542(7642):433\u0026ndash;438\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewman S, Hermetz KE, Weckselblatt B, Rudd MK (2016) Next-generation sequencing of duplication CNVs reveals that most are tandem and some create fusion genes at breakpoints. Am J Hum Genet 98(5):967\u0026ndash;983\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEising E, Carrion-Castillo A, Vino A et al (2019) A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Neuroimage Clin 23:101842\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJensen FE, Bonde L (2020) Integrating molecular and imaging modalities in understanding epilepsy. Epilepsy Res 163:106333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeldon M, Kilinc M, Holder JL Jr et al (2018) Clinical variability in SYNGAP1-related intellectual disability and epilepsy. Epilepsia 59(5):e56\u0026ndash;e63\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeili F et al (2021) Multi-parametric analysis of 57 SYNGAP1 variants reveals functional diversity across variants and assays. Epilepsy Res 174:106650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33308442/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33308442/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2021) Longitudinal neuroimaging in SYNGAP1-related disorders reveals disrupted connectivity. Brain 144(12):3570\u0026ndash;3585\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi B, Sun P, Gao Z, Huang L, Zhang Z, Zhang H (2023) Identification and functional characterization of de novo SYNGAP1 variant causing intellectual disability. Front Genet 14:1270175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2023.1270175\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2023.1270175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang X, Wang L, Zhou Y et al (2021) Genetic modifiers and phenotypic variability in neurodevelopmental disorders: insights from clinical cohorts. Front Neurosci 15:642563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2021.642563\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2021.642563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChildren\u0026rsquo;s Hospital of Philadelphia (CHOP) Researchers Identify Instances of SYNGAP1-Related Disorders Caused by Inherited Genetic Variants. News Release; June 9, 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chop.edu/news/childrens-hospital-philadelphia-researchers-identify-instances-syngap1-related-disorders\u003c/span\u003e\u003cspan address=\"https://www.chop.edu/news/childrens-hospital-philadelphia-researchers-identify-instances-syngap1-related-disorders\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayo Clinic (Elsevier Pure). Mutations in SYNGAP1 Cause Intellectual Disability, Autism, and a Specific Form of Epilepsy by Inducing Haploinsufficiency. Portal record (2013) Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mayoclinic.elsevierpure.com/en/publications/mutations-in-syngap1-cause-intellectual-disability-autism-and-a-s\u003c/span\u003e\u003cspan address=\"https://mayoclinic.elsevierpure.com/en/publications/mutations-in-syngap1-cause-intellectual-disability-autism-and-a-s\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\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":"SYNGAP1 gene, developmental epileptic encephalopathy, genotype-phenotype correlation, haploinsufficiency, intellectual disability, autism spectrum disorder","lastPublishedDoi":"10.21203/rs.3.rs-7635804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7635804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePathogenic variants in \u003cem\u003eSYNGAP1\u003c/em\u003e are a major cause of developmental and epileptic encephalopathy, typically presenting with intellectual disability, epilepsy, and autism spectrum disorder. Despite increasing recognition worldwide, genotype\u0026ndash;phenotype data from Central and Eastern Europe remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a nationwide study of 30 unrelated Polish pediatric patients carrying pathogenic or likely pathogenic \u003cem\u003eSYNGAP1\u003c/em\u003e variants. Variant classification followed ACMG/ClinGen guidelines. Clinical phenotyping used a structured numerical scoring system. Genotype\u0026ndash;phenotype relationships were explored with non-metric multidimensional scaling and PERMANOVA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified 30 pathogenic or likely pathogenic variants, including truncating, splice-site, and missense substitutions; 50% were novel. Haploinsufficiency emerged as the main pathogenic mechanism, though some missense variants suggested additional effects. Phenotypic analysis showed marked heterogeneity, but severe global developmental delay, intellectual disability, and epilepsy were consistently observed. Epilepsy affected\u0026thinsp;\u0026gt;\u0026thinsp;80% of patients and frequently required polytherapy. Genotype\u0026ndash;phenotype clustering demonstrated broader symptom variability in missense carriers compared with truncating or splice-site variants.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis first national cohort study from Poland broadens the \u003cem\u003eSYNGAP1\u003c/em\u003e mutational spectrum and highlights the consistently severe neurodevelopmental phenotype, particularly epilepsy and intellectual disability. Our results refine genotype\u0026ndash;phenotype correlations, emphasize the clinical impact of haploinsufficiency, and provide a framework for patient stratification in future trials and emerging therapeutic approaches.\u003c/p\u003e","manuscriptTitle":"Molecular Characterization and Genotype-phenotype Correlations of SYNGAP1 Variants in a Polish Pediatric Cohort With Neurodevelopmental Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:03:27","doi":"10.21203/rs.3.rs-7635804/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":"989073a5-6b54-425e-a794-eee71c1e2046","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T08:41:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:03:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7635804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7635804","identity":"rs-7635804","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}