Undiagnosed Diseases Program-Istanbul (UDP-IST): Diagnostic Impact of Systematic Genomic Reanalysis with Deep Phenotyping in 121 Patients

Undiagnosed Diseases Program-Istanbul (UDP-IST): Diagnostic Impact of Systematic Genomic Reanalysis with Deep Phenotyping in 121 Patients | 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 Undiagnosed Diseases Program-Istanbul (UDP-IST): Diagnostic Impact of Systematic Genomic Reanalysis with Deep Phenotyping in 121 Patients Ozlem Akgun-Dogan, Ozkan Ozdemir, Gulsah Sebnem Ozkose-Iyigel, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8842818/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Despite transformative advances in next-generation sequencing, a large proportion of patients with suspected rare genetic disorders remain without a molecular diagnosis after initial testing. Increasing evidence indicates that this persistent diagnostic gap often reflects limitations in variant interpretation and phenotypic resolution rather than the true absence of a genetic etiology. Here, we report the two-year experience of the Undiagnosed Diseases Program-Istanbul (UDP-IST), a structured, clinician-led, multidisciplinary program integrating deep phenotyping with systematic genomic reanalysis. Results We evaluated 121 individuals with previously unsolved rare diseases, achieving an overall diagnostic yield of 38%. The majority of diagnoses (87,0%) were obtained through genomic reanalysis alone, while resequencing provided additional diagnostic value in selected cases. Diagnostic success was driven by refined phenotype-genotype correlation, access to raw sequencing data, and evolving gene-disease knowledge. Conclusions Our findings demonstrate the clinical value and scalability of structured genomic reanalysis frameworks and support periodic reanalysis as a core component of longitudinal care for patients with suspected genetic disorders, particularly in resource-constrained healthcare settings. Deep phenotyping reanalysis undiagnosed disease program Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND Rare diseases pose a substantial and persistent challenge for genomic medicine due to their marked clinical and genetic heterogeneity [ 1 , 2 ]. Despite the transformative impact of next-generation sequencing (NGS) on rare disease diagnostics, a considerable proportion of patients remain undiagnosed following initial testing [ 3 , 4 ]. In many cases, the failure to achieve a diagnosis reflects interpretive limitations rather than the true absence of a genetic cause, as previously “negative” genomic findings may become interpretable as knowledge and analytical frameworks evolve [ 3 – 5 ]. Variant interpretation is inherently dynamic, shaped by ongoing advances in genotype-phenotype understanding, expansion of population reference databases, refinement of in silico prediction tools, and improvements in bioinformatic pipelines [ 4 , 5 ]. In parallel, incomplete phenotypic characterization at the time of initial testing may further hinder accurate genotype-phenotype correlation, leading to missed or deprioritized candidate variants [ 3 , 6 ]. Systematic reanalysis has therefore emerged as an effective and cost-efficient strategy for solving undiagnosed rare diseases. By reinterpreting raw sequencing data using updated analytical pipelines, expanded genomic resources, and refined clinical information derived from deep phenotyping, reanalysis can substantially increase diagnostic yield without the need for immediate resequencing [ 4 , 5 ]. Notably, the success of reanalysis depends not only on technical advances but also on specialized expertise; clinicians and bioinformaticians with dedicated experience in rare disease genomics remain limited in number. Structured multidisciplinary frameworks that integrate clinicians from diverse medical specialties with bioinformatics expertise are thus critical for improving diagnostic outcomes [ 3 , 7 ]. In response to these needs, several countries have established Undiagnosed Diseases Programs (UDPs) that combine systematic phenotyping, harmonized genomic reanalysis, and multidisciplinary case review within structured workflows [ 2 , 3 , 8 ]. By centralizing clinical and bioinformatic expertise, these programs provide an effective framework for addressing complex, previously unsolved cases consistently demonstrating improved diagnostic outcomes [ 5 , 6 , 9 ]. Beyond their clinical impact, UDPs have contributed to the discovery of novel disease mechanisms and the expansion of genotype-phenotype associations. Nevertheless, while structured UDPs are increasingly adopted, real-world data remain limited, and experiences from middle-income healthcare systems are still underrepresented in the literature [ 2 , 8 ]. Acibadem University Rare Diseases and Orphan Drugs Application and Research Center (ACURARE), established in 2017, serves as a national academic hub for rare disease research in Istanbul, the most populated city in Türkiye. Between 2021 and 2024, the İSTisNA platform was implemented as a structured capacity-building initiative aimed at establishing a coordinated ecosystem for undiagnosed and rare diseases by bringing together scientists, public institutions, patient organizations, and biobanks, while strengthening multidisciplinary evaluation, deep phenotyping, genomic analysis workflows, and workforce development. Building on this infrastructure, the Undiagnosed Diseases Program-Istanbul (UDP-IST) was launched in January 2024 as the first structured, multidisciplinary program in the country dedicated to patients with unsolved genetic disorders. In this study, we present the outcomes of UDP-IST, reporting two years of data from a cohort of 121 individuals with undiagnosed rare diseases. We evaluate the diagnostic impact of a standardized framework integrating deep phenotyping, systematic genomic reanalysis, and targeted resequencing, providing real-world evidence for the clinical value of structured undiagnosed disease programs. METHODS Program Overview This study reports the experience of UDP-IST, a structured program established at ACURARE to evaluate patients with undiagnosed rare diseases through systematic phenotyping and genomic reanalysis (Fig. 1). The analysis includes data collected during the program’s first two years. Figure 1. The illustration summarizes the UDP-IST diagnostic workflow for referred patients. Referral and Case Submission Patients who had undergone WES or WGS without a definitive molecular diagnosis were considered for evaluation. Referrals to UDP-IST were made by clinicians, including clinical geneticists, pediatricians, neurologists, and other subspecialties involved in clinical care. Submissions were made through an ACURARE-managed online portal ( https://istudp.istisna.org/Auth?ReturnUrl=%2F ) enabling upload of clinical summaries, pedigree, laboratory, and imaging data, previous genetic reports, raw sequencing files (FASTQ, BAM, or VCF), and written informed consent authorizing genomic data review and secure biobanking. Each unsolved case underwent an initial eligibility assessment by the UDP-IST coordination team. This eligibility assessment included review of informed consent forms, available clinical information, and evaluation of the technical quality of the uploaded genomic data to determine the feasibility of reanalysis. Cases meeting eligibility criteria proceeded to structured deep phenotyping and standardized genomic reanalysis performed using the in-house GenNext bioinformatics pipeline within the UDP-IST workflow (Fig. 1). All submitted clinical documentation, genomic datasets, and consent forms were archived within the Acibadem University Biobank, which operates under institutional governance and national regulatory requirements. Biospecimens and associated data are stored in monitored, access-controlled facilities to ensure secure, compliant, and traceable long-term preservation. Deep Phenotyping All eligible unsolved cases underwent a structured deep phenotyping process by the UDP-IST clinical team led by two experienced clinical geneticists (OAD and YA) who are active members of the UDNI-DWG (Undiagnosed Disease Network International-Diagnostic Working Group). Initial case discussions were conducted via online meetings with the referring clinician, during which medical history, symptom evolution, prior evaluations, and ambiguous or incomplete clinical features were systematically reviewed. Particular attention was given to resolving inconsistencies and capturing subtle but clinically relevant findings that may have been underrepresented in referral documentation. This collaborative review enabled the UDP-IST clinical team to develop a comprehensive understanding of the patient’s clinical presentation prior to phenotypic annotation. Following this review, clinical features were systematically encoded using standardized Human Phenotype Ontology (HPO) terminology. This approach generated a comprehensive, up-to-date phenotypic profile for each patient, which formed the basis for downstream genomic interpretation. Genomic Data Reanalysis, and Resequencing All available genomic data was reanalyzed using ACURARE’s in-house bioinformatics pipeline, GenNext workflow (Geniva Informatics; https://github.com/GenivaInformatics/gennext-workflows/tree/main/workflows ). Raw sequencing data (FASTQ), alignment files (BAM), or variant call files (VCF) were reprocessed through a unified pipeline to ensure analytical consistency across datasets. Variant annotation incorporated population frequency databases (gnomAD) [ 10 ], disease databases (OMIM [ 11 ], ClinVar [ 12 ], HGMD [ 13 ]), and in silico prediction tools including CADD [ 14 ], REVEL [ 15 ], VAMPP [ 16 ], and SpliceAI [ 17 ]. Expanded splice-site analysis and copy-number variant (CNV) screening were performed when technically feasible. All variants were evaluated in the context of the refined HPO-based clinical phenotype derived from deep phenotyping and classified according to ACMG/AMP criteria [ 18 ]. All genomic and clinical findings were reviewed in multidisciplinary, case-based online meetings involving clinical geneticists, the primary clinician responsible for the patient’s follow-up, relevant subspecialists from other clinical disciplines, and bioinformaticians. These meetings served as the central decision-making platform for integrating refined phenotypic information with genomic findings and determining subsequent diagnostic steps. Resequencing was performed when additional analysis was considered necessary after multidisciplinary review. In selected families, this included expanding prior singleton WES to trio or quad analyses by incorporating additional family members to support variant interpretation and segregation analysis. In cases where previous WES data were technically inadequate, due to insufficient coverage, low read depth, or poor overall data quality, a new WES or WGS analysis was performed. Diagnostic Classification and Reporting Cases were classified as solved or unsolved based on multidisciplinary consensus. Solved cases included patients with pathogenic (P) or likely pathogenic (LP) variants, as well as those with variants of uncertain significance (VUS) considered “clinically relevant” with genotype-phenotype correlation. Unsolved cases comprised patients with negative findings or variants deemed unrelated to the clinical presentation. For all cases, solved or unsolved, a technical report summarizing the genomic analysis and its clinical interpretation was generated and returned to the referring clinician. When additional clarification or counseling was required, patients and families were invited to the ACURARE UDP-IST Experience Center for in-person visits. Genetic counseling was provided focusing on interpretation of the results, clinical implications, and potential follow-up or reanalysis strategies. Patients who remained undiagnosed/unsolved after completion of the UDP-IST workflow were considered candidates for referral to UDNI-DWG. Potential data-sharing and referral processes were discussed with families. RESULTS Cohort Characteristics Between January 2024 and October 2025, 121 patients were consecutively evaluated within the UDP-IST workflow (Fig. 1). The cohort included 48 females (39.7%) and 73 males (60.3%), with a mean age at first referral of 7.44 years (SD 6.61; 95% CI 6.25–8.63) (Table 1 ). Most patients were referred during childhood, with 92,6% evaluated before 18 years of age; the largest age group was 5–12 years (Fig. 2a). The majority of individuals were of Turkish nationality (113/121, 93.4%), while 8 patients (6.6%) were international referrals. Parental consanguinity was reported in 38 families (31.4%), and family history suggestive of inherited disease was documented in 28.1% of cases (Table 1 ). The mean interval between initial WES/WGS analysis to reanalysis within UDP-IST was 21.7 months (SD 18.85; 95% CI 18.18–25.17). Table 1 Demographic and clinical characteristics of the UDP-IST cohort Gender n Percentage Female 48 39,67% Male 73 60,33% Age at first referral 0–2 14 11,57% ≥ 2–5 33 27,27% ≥ 5–12 51 42,15% ≥ 12–18 14 11,57% ≥ 18 9 7,44% Nationality Turkish 113 93,39% International 8 6,61% Consanguinity Reported Turkish 35 28,93% International 3 2,48% Denied 83 68,60% Affected Family Member Yes 34 28,10% No 87 71,90% Primary Phenotype NDD 83 68,60% Others Musculoskeletal 24 19,83% Cardiovascular 3 2,48% Dysmorphism 3 2,48% Gastrointestinal 2 1,65% Ocular 2 1,65% Immune-system 2 1,65% Hematological 1 0,83% Inborn Errors of Metabolism 1 0,83% Previous Genetic Testing Broad-range NGS WES 93 WGS 24 WES + WGS 4 Other Array 29 Chromosome 21 Single Gene Testing 12 Gene Panel 16 Mitochondrial 1 Other 1 Reanalysis Test Design WES Solo 84 69,42% Trio 5 4,13% Quadro 4 3,31% WGS Solo 22 18,18% Duo 2 1,65% Trio 4 3,31% Resequencing Test Design WES Trio 1 5,00% Quadro 1 5,00% WGS Solo 15 75,00% Trio 2 10,00% RNA Trio 1 5,00% Figure 2. Age distribution and expansion of phenotypic annotation through deep phenotyping in UDP-IST cohort. (a) Age distribution of patients at first referral to the UDP-IST. (b) Mean number of Human Phenotype Ontology (HPO) terms at initial genetic testing and at reanalysis, demonstrating a marked increase in phenotypic annotation following deep phenotyping. Neurodevelopmental disorders (NDD) were the most common indication for referral, accounting for 83 patients (68.6%). The remaining 38 cases (31.4%) comprised a heterogeneous group, most frequently involving musculoskeletal disorders, along with other system-related conditions (Table 1 ). Prior genetic testing other than WES or WGS had been performed in 63 patients (52.1%), most commonly chromosomal microarray analysis and karyotyping, followed by gene panel testing and single-gene analyses (Table 1 ). Table 1 summarizes patient demographics, primary phenotypes, prior genetic testing, and genomic testing approaches in the UDP-IST cohort. Deep-phenotyping and Reanalysis/Resequencing Deep phenotyping resulted in a marked increase in phenotypic annotation. The mean number of HPO terms per patient increased from 6.6 (SD 7.01; 95% CI 5.32–7.96), based on information documented in the initial genetic testing reports, to 13.6 (SD 7.67; 95% CI 12.26–15.03) following evaluation within the UDP-IST framework, corresponding to an absolute increase of 7 HPO terms and an approximate two-fold enrichment of phenotypic detail (Fig. 2b). Genomic reanalysis was most frequently performed using singleton WES (84/121, 69.4%), with trio (5/121, 4.1%) and quad (4/121, 3.3%) designs applied in selected cases. WGS reanalysis was performed in 28 patients (23.1%), predominantly as singleton analyses. Resequencing was performed in a subset of patients, including WES in 2 cases (1.7%) and WGS in 17 cases (14.0%), with family-based designs (trio, or quad) used when indicated to support variant discovery and interpretation (Table 1 ). Among the datasets accepted for reanalysis, most consisted of raw sequencing data: 115 of 121 cases (95.0%) were submitted as FASTQ files, 5 as VCF files, and 1 as a BAM file. Most datasets were generated in 2021 or later (94/121, 77.7%), with the remainder generated between 2015 and 2020 (Fig. 3). Figure 3 Distribution of raw sequencing data creation year by diagnostic outcome in reanalyzed cases. The majority of reanalysis-based diagnoses were observed among cases with sequencing data generated after 2021, coinciding with improvements in sequencing quality, coverage, and interpretative pipelines over time. Diagnostic Outcomes Within the UDP-IST framework, a definitive or clinically relevant genetic diagnosis was established in 46 of 121 patients, corresponding to an overall diagnostic yield of 38.0% (Fig. 4). Among the solved cases, 23 patients (19.0%) harbored P/LP variants, and 23 (19.0%) had VUSs considered clinically relevant based on multidisciplinary evaluation and genotype-phenotype correlation. The remaining patients (62.0%) were classified as unsolved, as no variant explaining the clinical phenotype was identified (Fig. 4). Most diagnoses were achieved through reanalysis, which solved 40 of 46 cases (87.0%), whereas 6 cases (13.0%) were solved following resequencing. Among reanalysis-based diagnoses, WES was the predominant modality (34 cases, 85.0%), followed by WGS (6 cases, 15.0%). In the resequencing group, diagnoses were obtained using both WES and WGS. Two cases were solved by family-based WES (one trio (P36) and one quad (P1)), while the remaining four cases were resolved by solo whole-genome sequencing (Fig. 4). The time interval between the initial genetic test and reanalysis slightly differed between the solved and unsolved groups. Among patients undergoing reanalysis, the mean interval between the initial genetic test and reanalysis was longer in the solved group (22.83 months; median, 18 months, CI 95% [16.20-29.47]) than in the unsolved group (20,82 months; median, 13 months, CI 95% [16.34–25.30]. Although this difference did not reach statistical significance, it indicates a trend toward diagnostic success being observed after a longer interval, approximately 1.5 to 2 years, between the initial test and reanalysis. Figure 4. Diagnostic outcomes and contribution of reanalysis and resequencing within the UDP-IST cohort. The figure summarizes the overall diagnostic yield and the relative contribution of genomic reanalysis and resequencing strategies to diagnosis among the 121 patients evaluated within the UDP-IST framework. Phenotype-Specific Diagnostic Yield and Variant Characteristics Diagnostic yield varied across phenotypic categories. Among patients with NDD, 30 of 83 (36.1%) were solved. Higher proportional yields were observed in selected non-NDD groups, most notably among patients with musculoskeletal phenotypes. No diagnoses were established among patients referred primarily for dysmorphism features, metabolic, or vascular phenotypes (Fig. 5). Figure 5. Phenotype-specific diagnostic yield in the UDP-IST cohort. (IEM*: Inborn errors of metabolism). Distribution of primary phenotypic categories and corresponding diagnostic outcomes (solved vs. unsolved) among 121 patients, with gene-level summary of diagnosed cases shown on the right. Inheritance Patterns and Variant Characteristics In solved cases, autosomal dominant inheritance accounted for 81.3% of diagnoses, followed by autosomal recessive inheritance in 18.7% (Fig. 6a). Cases involving candidate genes without established gene-phenotype associations were excluded from inheritance analyses. Across 46 solved patients, 51 clinically relevant variants in 45 genes were identified, with missense variants predominating at 80.4%. Truncating variants, including frameshift and nonsense variants, collectively accounted for 11.8%, while copy-number variants, splice-site variants, repeat expansions, and non-coding variants each represented 7.8% of variants (Fig. 6b). Twenty five of the identified variants were novel, including 10 classified as pathogenic or likely pathogenic and 15 classified as variants of uncertain significance. Figure 6. Inheritance patterns and variant characteristics among solved cases in the UDP-IST cohort. (a) Distribution of inheritance patterns among solved patients, with autosomal dominant diagnoses predominating, followed by autosomal recessive. Cases involving candidate genes without established gene-phenotype associations were excluded from inheritance analysis. (b) Distribution of clinically relevant variant types identified in solved cases, stratified by zygosity. Missense variants constituted the majority, followed by loss-of-function variants, while splice-site variants, copy-number variants, repeat expansions, and non-coding variants were observed less frequently. Reanalysis-based Diagnoses Reanalysis emerged as the primary driver of diagnostic resolution, accounting for 87.0% of solved cases. Two complementary factors underpinned diagnostic success within this group. In a subset of patients (7/40, 17.5%), diagnostic variants were identified in genes for which gene-phenotype associations or relevant genotype-phenotype correlations became established after initial genetic testing, enabling diagnosis through updated gene-disease knowledge. In the remaining reanalysis-based diagnoses, causative variants were identified in genes that were already recognized as disease-associated at the time of the initial genetic testing. In these cases, diagnostic resolution was primarily supported by improved phenotype-genotype alignment achieved through systematic deep phenotyping, reflected by an increased number of HPO terms incorporated prior to reanalysis. This refinement of phenotypic information directly facilitated variant prioritization and strengthened clinical interpretation. Details of the variants identified in patients diagnosed through reanalysis are summarized in Table 2 . All HPO terms used for phenotypic annotation and genotype-phenotype correlation analyses in solved cases are summarized in Supplementary Table 1. Table 2 Molecular and phenotypic characteristics of variants identified in solved cases through genomic reanalysis and resequencing in the UDP-IST cohort Patient ID Gene Transcript ID Variant Variant Type Amino Acid Change (if applicable) Zygosity Result Diagnosis* Main Phenotype Diagnosis Through Reanalysis P2 CACNA1S NM_000069.3 c.4081T > A Missense p.Phe1361Ile Heterozygous Relevant VUS Hypokalemic periodic paralysis, type 1 (#170400) AD NDD P5 COL4A2 NM_001846.4 c.4987G > A Missense p.Gly1663Ser Heterozygous Relevant VUS Brain small vessel disease 2A, autosomal dominant (#614483) AD NDD P11 WDR62 NM_001083961.2 c.3936dup Truncating Frameshift p.Val1313ArgfsTer18 Heterozygous Relevant VUS Microcephaly 2, primary, autosomal recessive, with or without cortical malformations (#604317) AR NDD P16ᵃ SUZ12 NM_015355.4 c.167C > T Missense p.Ser56Phe Heterozygous Relevant VUS Imagawa-Matsumoto syndrome (#618786) AD NDD P20 KCNH1 NM_172362.3 c.1486G > A Missense p.Gly496Arg Heterozygous LP/P Temple-Baraitser syndrome (#611816) AD NDD P26ᵃ TNPO2 NM_001382241.1 c.191C > T Missense p.Ser64Phe Heterozygous Relevant VUS Intellectual developmental disorder with hypotonia, impaired speech, and dysmorphic facies (#619556) AD NDD P27ᵃ COL12A1 NM_004370.5 c.1337A > C Missense p.Asp446Ala Heterozygous Relevant VUS Bethlem myopathy 2 (#616471) AD Musculoskeletal P28 GFI1B NM_001377304.1 c.569G > A Missense p.Arg190Gln Heterozygous Relevant VUS Bleeding disorder, platelet-type, 17 (#187900) AD Hematological P29ᵃ B3GALT6 NM_080605.4 c.235A > G Missense p.Thr79Ala Compound Heterozygous (in trans) LP/P Spondyloepimetaphyseal dysplasia with joint laxity, type 1, with or without fractures (#271640) AR Musculoskeletal c.467A>Cᵃ Missense p.Asp156Ala P31ᵃ CHD3 NM_001005273.3 c.2973G > A Missense p.Met991Ile Heterozygous Relevant VUS Snijders Blok-Campeau syndrome (#618205) AD NDD P32** HPDL NM_032756.4 c.149G > A Missense p.Gly50Asp Homozygous LP/P Spastic paraplegia 83, autosomal recessive (#619027) AR NDD P34ᵃ LFNG NM_001040167.2 c.1073G > A Missense p.Arg358Lys Homozygous LP/P Spondylocostal dysostosis 3, autosomal recessive (#609813) AR Musculoskeletal P38 FBXO11 NM_001190274.2 c.412A > G Missense p.Arg138Gly Heterozygous LP/P Intellectual developmental disorder with dysmorphic facies and behavioral abnormalities (#618089) AD NDD P44 Novel Gene Candidate (Unpublished Results) Cardiovascular P48 Novel Gene Candidate (Unpublished Results) Musculoskeletal P51 Novel Gene Candidate (Unpublished Results) NDD P53ᵃ ATP9A NM_006045.3 c.817G > A Missense p.Val273Ile Homozygous Relevant VUS Neurodevelopmental disorder with poor growth and behavioral abnormalities (#620242) AR NDD P54 COPA NM_004371.4 c.3533G > A Missense p.Arg1178His Heterozygous Relevant VUS Autoinflammation and autoimmunity, systemic, with immune dysregulation 1 (#616414) AD Immune-system P55ᵃ ITPR3 NM_002224.4 c.812A > G Missense p.Gln271Arg Heterozygous Relevant VUS Charcot-Marie-Tooth disease, demyelinating, type 1J (#620111) AD Musculoskeletal P56 COL12A1 NM_004370.6 c.4418-1G > A Splice Site - Heterozygous LP/P Bethlem myopathy 2 (#616471) AD NDD P63ᵃ AGO2 NM_012154.5 c.1805C > A Missense p.Pro602His Heterozygous LP/P Lessel-Kreienkamp syndrome (#619149) AD NDD P67 POGZ NM_015100.4 c.2989C > T Nonsense p.Arg997Ter Heterozygous LP/P White-Sutton syndrome (#616364) AD Gastrointestinal P68** SMARCA2 NM_001289396.1 c.1514G > A Missense p.Arg505Gln Heterozygous LP/P Blepharophimosis-impaired intellectual development syndrome (#619293) AD NDD P72** KMT2C NM_170606.3 c.1436A > C Missense p.Gln479Pro Heterozygous LP/P Kleefstra syndrome 2 (#617768) AD NDD P74ᵃ HCN1 NM_021072.4 c.1253T > C Missense p.Met418Thr Heterozygous Relevant VUS Developmental and epileptic encephalopathy 24 (#615871) AD NDD P75** COL6A3 NM_004369.4 c.7024C > T Nonsense p.Arg2342Ter Homozygous LP/P Ullrich congenital muscular dystrophy 1C (#620728) AD, AR NDD NEB NM_001164508.2 c.25183C > T Nonsense p.Arg8395Ter Homozygous Nemaline myopathy 2, autosomal recessive (#256030) AR P77 ASXL3 NM_030632.3 c.2471C > T Missense p.Pro824Leu Heterozygous LP/P Bainbridge-Ropers syndrome (#615485) AD NDD P85ᵃ POGZ NM_015100.4 c.2571G > T Missense p.Arg857Ser Heterozygous LP/P White-Sutton syndrome (#616364) AD NDD P88ᵃ TUBA1A NM_006009.4 c.514T > C Missense p.Tyr172His Heterozygous LP/P Lissencephaly 3 (#611603) AD NDD P90** SCN1A NM_001165963.4 c.4769T > C Missense p.Leu1590Pro Heterozygous LP/P Developmental and epileptic encephalopathy 6B, non-Dravet (#619317) AD NDD P96 KIF7 NM_198525.3 c.2272G > A Missense p.Glu758Lys Compound Heterozygous (in trans) Relevant VUS Acrocallosal syndrome (#200990) AR NDD c.259A > G Missense p.Asn87Asp P98ᵃ ADNP NM_001282531.3 c.1811C > T Missense p.Pro604Leu Heterozygous Relevant VUS Helsmoortel-van der Aa syndrome (#615873) AD NDD P100ᵃ CACNB2 NM_201596.3 c.1421C > G Missense p.Pro474Arg Heterozygous Relevant VUS Brugada syndrome 4 (#611876) AD Cardiovascular P102ᵃ CACNA1G NM_018896.5 c.1284G > C Missense p.Glu428Asp Heterozygous Relevant VUS Spinocerebellar ataxia 42, early-onset, severe, with neurodevelopmental deficits (#618087) AD NDD P103 SMO NM_005631.5 c.1285A > T Missense p.Ile429Phe Homozygous LP/P Pallister-Hall-like syndrome (#241800) AR Musculoskeletal P111ᵃ GNB2 NM_005273.4 c.233A > G Missense p.Lys78Arg Heterozygous Relevant VUS Sick sinus syndrome 4 (#619464) AD Cardiovascular P113ᵃ FXN NM_000144.5 c.2T > Gᵃ Missense p.Met1Arg Compound Heterozygous (in trans) LP/P Friedreich ataxia (#229300) AR NDD NM_000144.6 c.165 + 1340 GAA [ 9 ] GAA[> 219] Repeat expansion - P116ᵃ ASH1L NM_018489.3 c.8470G > A Missense p.Val2824Ile Heterozygous Relevant VUS Intellectual developmental disorder, autosomal dominant 52 (#617796) AD NDD P118ᵃ VPS4A NM_013245.3 c.746C > A Missense p.Thr249Lys Heterozygous LP/P CIMDAG syndrome (#619273) AD NDD P119ᵃ TCOF1 NM_001371623.1 c.2429del Truncating Frameshift p.Gly810GlufsTer18 Heterozygous LP/P Treacher Collins syndrome 1 (#154500) AD Musculoskeletal Diagnosis Through Resequencing P1ᵃ CFAP410 NM_004928.3 c.322C > T Missense p.Arg108Cys Homozygous LP/P Spondylometaphyseal dysplasia, axial (#602271) AR Ocular P12ᵇ RAP1B NM_001010942.3 c.-27 + 2T > A Missense p.Tyr324Cys Heterozygous Relevant VUS Thrombocytopenia 11 with multiple congenital anomalies and dysmorphic facies (#620654) AD Musculoskeletal P36ᵇ NOTCH1 NM_017617.5 c.4787T > C Missense p.Leu1596Pro Heterozygous LP/P PMID: 38778082 NDD P61 RMRP NR_003051.4 n.94dup Noncoding - Homozygous LP/P Metaphyseal dysplasia without hypotrichosis (#250460) AR Musculoskeletal P69ᵃ OGDHL NM_018245.3 c.1826G>Tᵃ Missense p.Ser609Ile Compound Heterozygous (in trans) Relevant VUS Yoon-Bellen neurodevelopmental syndrome (#619701) AR NDD c.2026G>Aᵃ Missense p.Val676Ile P73 WLS NM_024911.7 c.1261C > T Missense p.Arg421Trp Homozygous LP/P Zaki syndrome (#619648) AR NDD * Diagnoses are classified according to OMIM phenotypes. ** Indicates cases in which diagnostic variants were identified in genes for which gene–phenotype associations or relevant genotype–phenotype correlations became established only after the initial testing. ᵃ Indicates cases in which a novel variant was identified. ᵇ Indicates cases in which diagnostic variants were identified in genes for which gene–phenotype associations or relevant genotype–phenotype correlations became established only after the initial testing and in which a novel variant was identified. Resequencing-based Diagnoses Further diagnostic testing, including trio or quad WES, WGS, and RNA sequencing, was performed in 20 patients who remained undiagnosed after reanalysis. Through these resequencing approaches, an additional six patients achieved a molecular diagnosis. In patients undergoing trio or quad WES, a molecular diagnosis was achieved in all cases. In one patient (P36), the causative variant had been present in the reanalysis dataset but could not be interpreted as diagnostic at the time, as the relevant phenotypic expansion of the implicated gene had not yet been described in the literature. An updated literature search enabled recognition of the variant as causative following resequencing. In another patient (P1), the missed diagnosis was attributed to insufficient exon-level coverage in the initial reanalysis. The causative variant was present at low coverage, failed quality filtering thresholds, and was therefore filtered during reanalysis. Resequencing achieved adequate coverage of the relevant genomic region, allowing reliable detection and interpretation of the variant. WGS led to diagnostic findings in four patients whose causative variants could not be detected through reanalysis due to multiple underlying factors. In the first patient (P69), the diagnosis was established in a gene for which the disease association had been reported after the reanalysis. In the second patient (P61), a pathogenic variant was identified in a long non-coding RNA gene ( RMRP ) that had not been captured by prior WES analysis. In the remaining two patients (P12 and P73), technical limitations of the initial analyses, restricted coverage and reliance on VCF-only data, precluded detection of the causative variants during reanalysis. Details of the variants identified in patients diagnosed through resequencing are summarized in Table 2 . In three patients (Patients 44, 48, and 51), variants were identified in candidate genes that have not previously been associated with the observed clinical phenotypes. These findings represent putative novel gene-phenotype associations, and further evaluation of the pathogenic relevance of the identified variants is ongoing through collaborative efforts and functional studies. Table 2 summarizes molecular findings in solved cases following genomic reanalysis or resequencing, including variant details, inheritance, associated OMIM diagnosis, and primary phenotype. Selected Cases Diagnosis by Reanalysis Through Emerging Gene-Phenotype Associations, P38 We report a 6-year-old female patient presenting with global developmental delay, behavioral abnormalities, dysmorphic facial features, and multisystem involvement. She was born at term to non-consanguineous, healthy parents. Developmental milestones were delayed, with marked speech impairment, autistic features, hyperactivity, bruxism, and poor attention. Physical examination revealed microcephaly, a high and prominent forehead, high palate, ptosis, sparse hair, congenital bilateral hip dislocation, cutaneous syndactyly of the toes, and hiatus hernia. Initial genetic analyses, including chromosomal microarray analysis and WES, were inconclusive. The WES FASTQ data were reanalyzed four years after the initial testing. Reanalysis identified a pathogenic, heterozygous, de novo, missense variant in FBXO11 (NM_001190274.2:c.412A > G; p.Arg138Gly). Reverse phenotyping demonstrated strong concordance between the patient’s clinical features and FBXO11 -related intellectual developmental disorder with dysmorphic facies and behavioral abnormalities (OMIM #618089), leading to a definitive molecular diagnosis. The initial WES was reported in June 2018, before FBXO11 was established as a morbid OMIM gene, precluding clinical interpretation at that time; the gene-phenotype association was reported shortly thereafter, in August 2018. This case highlights the importance of integrating comprehensive and up-to-date literature evidence beyond curated databases such as OMIM in the setting of rapidly evolving gene-phenotype knowledge. VCF-Only Reanalysis Masking CFAP410 -Related Skeletal Dysplasia, P1 We report a 20-year-old male patient presenting with optic atrophy, cone-rod dystrophy, strabismus, scoliosis, short stature, and brachydactyly, consistent with a multisystem skeletal dysplasia with ocular involvement. Parental consanguinity was denied. Previous genetic tests, including chromosomal analysis, chromosomal microarray analysis, and WES, were inconclusive. When reanalysis was planned within the UDP-IST framework, only VCF-format data were provided. Reanalysis based on VCF data did not identify a causative variant. Subsequently, quadro-based WES resequencing was performed, which identified an LP, homozygous missense variant in CFAP410 (NM_004928.3:c.322C > T; p.Arg108Cys). Reverse phenotyping demonstrated strong concordance between the patient’s clinical features and CFAP410 -related axial spondylometaphyseal dysplasia (OMIM #602271), leading to a definitive molecular diagnosis. This case highlights the diagnostic limitations of VCF-only reanalysis and underscores the critical importance of access to raw sequencing data, as the absence of raw files precludes reassessment of read-level evidence and coverage metrics, allowing pathogenic variants filtered during initial analyses to remain undetected. Non-coding RNA Genes as a Diagnostic Consideration in WES-Negative Patients, P61 We report an 8-year-old male patient presenting with disproportionate short stature, mild intellectual disability, and skeletal dysplasia. He was born at term to consanguineous parents, with prenatal ultrasonography showing shortened long bones. Developmental delay and characteristic skeletal features, including rhizomelia, brachydactyly, genu varum, and a waddling gait, were noted, along with recurrent infections and transient liver enzyme elevation. Previous genetic investigations, including chromosomal analysis, chromosomal microarray, mitochondrial DNA analysis, and WES, were inconclusive. Reanalysis of WES FASTQ data within the UDP-IST framework did not identify a causative variant. Subsequent WGS identified a homozygous insertion variant in the non-coding RNA gene RMRP (NR_003051.4:n.94dup), classified as likely pathogenic. Reverse phenotyping demonstrated strong concordance with RMRP - related disorders, including cartilage-hair hypoplasia, leading to a definitive molecular diagnosis. This case highlights the limitations of exome-based approaches for detecting pathogenic variants in non-coding RNA genes and underscores the added diagnostic value of genome-wide sequencing. Reanalysis Revealing a Hidden Second Allele in FXN , P113 We report an adolescent male patient presenting with progressive gait instability, cerebellar ataxia, dysmetria, dysdiadochokinesis, fatigue, and mild scoliosis. Early development was unremarkable; neurological symptoms evolved gradually during adolescence. Neurological examination and cranial MRI findings were consistent with spinocerebellar ataxia. Initial diagnostic evaluation included WGS, which did not identify a causative variant; subsequent targeted repeat expansion testing for common spinocerebellar ataxia subtypes was also negative. Friedreich ataxia-specific testing subsequently revealed a heterozygous GAA repeat expansion in the FXN gene, inherited from the mother. As no second pathogenic allele was identified at that time, this finding was interpreted as a carrier state. Reanalysis of the WGS data within the UDP-IST framework identified a heterozygous start-loss variant in FXN (c.2T > G; p.Met1Arg), inherited from the father. The presence of the GAA repeat expansion in trans with the start-loss variant established compound heterozygosity, leading to a definitive diagnosis of Friedreich ataxia. Reverse phenotyping confirmed concordance between the patient’s clinical features and the known FXN -related disease spectrum. DISCUSSION In this study, we present two-years experience of the Undiagnosed Diseases Program-Istanbul, demonstrating the clinical impact of a structured, clinician-led, multidisciplinary framework that integrates deep phenotyping with systematic genomic reanalysis. Using this approach, a genetic diagnosis was established in 38% of a highly complex cohort of patients with previously unsolved rare diseases, despite extensive prior genetic testing and predominant use of singleton sequencing. This diagnostic yield places UDP-IST within the upper range reported by established international programs, including the NIH Undiagnosed Diseases Network (UDN), the Australian Adult UDP (AHA-UDP), and national initiatives from Europe and Asia, where yields typically range from 25% to 45% [ 3 , 5 , 8 , 9 ]. Observed variability across programs likely reflects differences in cohort composition, referral pathways, availability of family-based data, and analytical strategies rather than intrinsic differences in program performance. In UDP-IST, a clinician-led referral model is employed, whereby case submission is initiated by the primary clinician rather than directly by patients or families. While patient-initiated application models used by some international programs may enhance accessibility, they may also increase the burden on limited expert resources, particularly in healthcare systems with constrained specialist capacity [ 3 ]. The clinician-led model adopted in UDP-IST facilitates focused triage, ensures that referrals are grounded in a strong suspicion of Mendelian disease, and enables more efficient allocation of genomic and bioinformatic expertise—an important consideration in middle-income healthcare systems such as Türkiye. A key observation of this study is that genomic reanalysis accounted for nearly three-quarters of all solved cases, underscoring its central role in diagnostic resolution. This proportion is comparable to, and in some instances exceeds, those reported by other undiagnosed disease programs, where reanalysis contributes to approximately 40–60% of diagnoses depending on cohort characteristics and analytical frameworks [ 4 , 5 ]. Taken together, these findings reinforce accumulating evidence that many initially negative or inconclusive genomic results reflect interpretive and contextual limitations rather than the true absence of a genetic etiology [ 3 – 5 ]. In line with this growing body of evidence, reanalysis success in UDP-IST was primarily driven by improved phenotype-genotype correlation achieved through deep phenotyping, the emergence of new gene-disease associations, and advances in bioinformatic pipelines. Deep phenotyping emerged as a critical contributor to diagnostic success in UDP-IST. Beyond structured data annotation, this process relied on iterative clinician-to-clinician dialogue, allowing refinement of subtle, evolving, or previously underrecognized clinical features. Notably, the central role of deep phenotyping in achieving high diagnostic yield has already been demonstrated by our group in a prior clinical whole-genome sequencing study, where systematic pre- and post-test phenotypic reassessment substantially enhanced variant interpretation and diagnostic yield [ 19 ]. This approach resulted in a marked increase in the number of HPO terms incorporated at reanalysis, highlighting the limitations of relying solely on phenotypic information captured at the time of initial genetic testing. Comparable benefits of systematic phenotypic reassessment have been reported across multiple undiagnosed disease programs, supporting its central role in enhancing diagnostic yield [ 5 , 7 ]. Notably, all reanalysis-based diagnoses in UDP-IST were achieved in cases with access to raw FASTQ data, whereas reanalysis restricted to BAM or VCF files did not yield diagnostic findings. The inability to reassess coverage metrics and read-level evidence when raw data are unavailable represents a major limitation of VCF/BAM-only reinterpretation, increasing the likelihood that pathogenic variants filtered during initial analyses remain undetected. Similar observations have been reported in other cohorts, supporting the notion that when raw sequencing data are unavailable, resequencing may be a more effective diagnostic strategy than repeated reanalysis [ 4 , 5 ]. Although reanalysis was the principal driver of diagnostic resolution, resequencing provided additional diagnostic value in selected cases. Repeat whole-exome sequencing was required in some patients to address limitations of the initial data, including low coverage or incomplete datasets, enabling reliable variant detection. In other cases, whole-genome sequencing extended the diagnostic scope beyond exome-based approaches, allowing identification of pathogenic variants in non-coding regions, poorly captured exons, or repeat-associated mechanisms. Together, these findings highlight the complementary role of repeat WES and WGS in specific clinical contexts, while underscoring ongoing considerations regarding the routine first-line use of genome-wide sequencing given cost, infrastructure, and interpretive demands [ 9 , 20 ]. Beyond individual diagnoses, structured UDPs have broader implications for healthcare systems and national rare-disease strategies. Genomic diagnoses achieved within UDP frameworks have been shown to reduce unnecessary investigations, shorten prolonged diagnostic odysseys, and inform targeted clinical management, collectively improving patient outcomes and cost-effectiveness [ 21 , 22 ]. In this regard, the UDP-IST model represents a pragmatic and scalable strategy for embedding genomic medicine into routine care and national rare-disease infrastructures, particularly in middle-income healthcare settings where resources must be carefully prioritized. CONCLUSIONS In conclusion, the early experience of UDP-IST demonstrates that a structured, multidisciplinary approach integrating deep phenotyping with systematic genomic reanalysis can yield substantial diagnostic benefit for patients with undiagnosed rare diseases. By emphasizing interpretation, phenotypic refinement, and coordinated clinical-bioinformatic expertise rather than immediate technological escalation, UDP-IST offers a scalable model with relevance beyond national boundaries. Importantly, our findings align with and extend current recommendations supporting periodic genomic reanalysis as a core component of longitudinal care for patients with suspected genetic disorders, particularly once sufficient time has elapsed for knowledge and analytical frameworks to evolve [ 4 , 5 , 23 ]. In this broader context, these findings underscore the transformative potential of the UDP-IST framework to inform clinical practice, optimize resource utilization, and shorten diagnostic odysseys, while providing a robust and evidence-based foundation for the future development of a coordinated national undiagnosed diseases network. Future integration of genome-wide and transcriptomic approaches for unresolved cases, coupled with international data-sharing and national registry development, will be critical to maximizing both clinical and translational impact. Declarations Ethical Statements This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Acibadem Mehmet Ali Aydinlar University (ATADEK: 2023-14/511). Written informed consent was obtained from patients and/or families to share and publish clinical and genetic information. Data Availability The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request, in accordance with ethical approvals and applicable data protection regulations. Competing Interest All other authors declare no competing interests or conflicts of interest. Funding This study was supported by the ISTisNA Project, funded by the Republic of Türkiye, Ministry of Industry and Technology, Istanbul Development Agency (ISTKA), Project No: TR10/19/FZD/0003. Author Contributions OAD: Project administration, Genetic analysis, Writing-original draft. OO: Software, Writing-review and editing. GSOI: Data curation, Genetic analysis, Writing-review and editing. BY: Data curation, Formal analysis, Writing-review and editing. UO: Data curation, Formal analysis, Writing-review and editing. EA: Data curation and Genetic analysis. AB: Data curation and Genetic analysis. AY: Data curation and Genetic analysis. GO: Data curation and Genetic analysis. HG: Wet-lab Investigation (Sanger sequencing). BA: Data curation. JCY: Data curation. FK: Data curation. ZY: Data curation. EDA: Data curation. OHN: Project administration, Writing-review and editing. KB: Writing-review and editing. YA: Conceptualization, Project administration, Writing-original draft. Acknowledgements We thank the patients and their families for their participation. We acknowledge the referring clinicians, collaborating clinicians, and medical students for their valuable contributions to this study. Author Information: Ozlem Akgun Dogan – [email protected] ORCID:0000-0002-8310-4053 Ozkan Ozdemir – [email protected] Gulsah Sebnem Ozkose Iyigel – [email protected] Berkay Yildiz – [email protected] Ulas Ozonur – [email protected] Aybike S. Bulut – [email protected] Eylul Aydin – [email protected] Ayca Yigit – [email protected] Gizem Onder – [email protected] Huma Gunay – [email protected] Beril Ay – [email protected] Julide Ceren Yilmaz – [email protected] Firuze Kokten – [email protected] Zeynep Yentur – [email protected] Emiran Defne Atay – [email protected] Kaya Bilguvar – [email protected] Ozden Hatirnaz Ng – [email protected] Yasemin Alanay – [email protected] References Boycott KM, et al. International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases. Am J Hum Genet. 2017;100(5):695–705. Taruscio D, et al. National registries of rare diseases in Europe: an overview of the current situation and experiences. Public Health Genomics. 2015;18(1):20–5. Splinter K, et al. Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease. N Engl J Med. 2018;379(22):2131–9. Wright CF, FitzPatrick DR, Firth HV. Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet. 2018;19(5):253–68. Garg N, et al. Reanalysis of Exome Sequencing Data in the Indian Undiagnosed Diseases Program: Improving Diagnostic Yield and Ending Diagnostic Odyssey. Clin Genet. 2025;107(6):620–35. Adedipe D, et al. Neurodevelopmental Phenotyping and Genotyping in the Pediatric National Institute of Health Undiagnosed Disease Program. Am J Med Genet B Neuropsychiatr Genet. 2025;198(8):230–40. Shi Y, et al. Accelerating rare disease detection: an experience of multidisciplinary team model in undiagnosed diseases program in a children's hospital. Front Public Health. 2024;12:1373649. Wallis M, et al. Experience of the first adult-focussed undiagnosed disease program in Australia (AHA-UDP): solving rare and puzzling genetic disorders is ageless. Orphanet J Rare Dis. 2024;19(1):288. Slaba K, et al. Diagnostic efficacy and clinical utility of whole-exome sequencing in Czech pediatric patients with rare and undiagnosed diseases. Sci Rep. 2024;14(1):28780. Karczewski KJ, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. McKusick-Nathans Institute of Genetic, Medicine. J.H.U.B., MD), Online Mendelian Inheritance in Man, OMIM® . Landrum MJ, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980–5. Stenson PD, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003;21(6):577–81. Rentzsch P, et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–94. Ioannidis NM, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99(4):877–85. Ozdemir O et al. Molecular and In Silico Analysis of the CHEK2 Gene in Individuals with High Risk of Cancer Predisposition from Turkiye. Cancers (Basel), 2024. 16(22). Jaganathan K, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535–e54824. Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. Akgun-Dogan O, et al. Impact of deep phenotyping: high diagnostic yield in a diverse pediatric population of 172 patients through clinical whole-genome sequencing at a single center. Front Genet. 2024;15:1347474. Albuquerque ALB, et al. Diagnostic Yield of Genome Sequencing Versus Exome Sequencing in Pediatric Patients With Rare Phenotypes: A Systematic Review and Meta-Analysis. Am J Med Genet A. 2025;197(10):e64146. Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med. 2022;14(1):23. Yang G, et al. The national economic burden of rare disease in the United States in 2019. Orphanet J Rare Dis. 2022;17(1):163. Miller DT, et al. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23(8):1381–90. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 13 Feb, 2026 First submitted to journal 12 Feb, 2026 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. 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patients.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/6c651a82e882289673566af4.png"},{"id":108944334,"identity":"c1d587eb-7e13-48e1-88ed-c44a25d49acb","added_by":"auto","created_at":"2026-05-11 05:58:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":312916,"visible":true,"origin":"","legend":"\u003cp\u003eAge distribution and expansion of phenotypic annotation through deep phenotyping in UDP-IST cohort. (a) Age distribution of patients at first referral to the UDP-IST. (b) Mean number of Human Phenotype Ontology (HPO) terms at initial genetic testing and at reanalysis, demonstrating a marked increase in phenotypic annotation following deep phenotyping.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/74e15aa7f9dbec4953b4fbbf.png"},{"id":108978045,"identity":"ae89ae68-8ba9-43c8-b30d-9959516959c3","added_by":"auto","created_at":"2026-05-11 11:33:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264676,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of raw sequencing data creation year by diagnostic outcome in reanalyzed cases. The majority of reanalysis-based diagnoses were observed among cases with sequencing data generated after 2021, coinciding with improvements in sequencing quality, coverage, and interpretative pipelines over time.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/1b8c0453ec266114ab5e2cda.png"},{"id":108977992,"identity":"750755ac-9032-4d90-9f72-a5cdccb51ede","added_by":"auto","created_at":"2026-05-11 11:33:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":595857,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic outcomes and contribution of reanalysis and resequencing within the UDP-IST cohort.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/ef87e688e8c3688f0a907eba.png"},{"id":108944405,"identity":"16c1093a-b31f-4c6f-a7c2-e666dc14e627","added_by":"auto","created_at":"2026-05-11 05:58:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":836182,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotype-specific diagnostic yield in the UDP-IST cohort. (IEM*: Inborn errors of metabolism). Distribution of primary phenotypic categories and corresponding diagnostic outcomes (solved vs. unsolved) among 121 patients, with gene-level summary of diagnosed cases shown on the right.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/05a3a58def45c5d2bff6c6a4.png"},{"id":108944298,"identity":"8cdab510-1a1b-4b0d-be72-076180290c78","added_by":"auto","created_at":"2026-05-11 05:58:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":362282,"visible":true,"origin":"","legend":"\u003cp\u003eInheritance patterns and variant characteristics among solved cases in the UDP-IST cohort. \u003cstrong\u003e(a)\u003c/strong\u003eDistribution of inheritance patterns among solved patients, with autosomal dominant diagnoses predominating, followed by autosomal recessive. Cases involving candidate genes without established gene-phenotype associations were excluded from inheritance analysis.\u003cstrong\u003e (b) \u003c/strong\u003eDistribution of clinically relevant variant types identified in solved cases, stratified by zygosity. Missense variants constituted the majority, followed by loss-of-function variants, while splice-site variants, copy-number variants, repeat expansions, and non-coding variants were observed less frequently.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/1373f9c0f45eb6bc35ba7ca3.png"},{"id":108979959,"identity":"05bfef49-b7de-4eeb-9937-6043abe528bb","added_by":"auto","created_at":"2026-05-11 12:02:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4063260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/d819ee19-5463-4f47-b315-3a869d616afd.pdf"},{"id":108944302,"identity":"cbb23630-02a6-4928-88a1-c7bf7e01d189","added_by":"auto","created_at":"2026-05-11 05:58:22","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":3788380,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8842818/v1/0f112afb5f7dc6966b115570.docx"}],"financialInterests":"","formattedTitle":"Undiagnosed Diseases Program-Istanbul (UDP-IST): Diagnostic Impact of Systematic Genomic Reanalysis with Deep Phenotyping in 121 Patients","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRare diseases pose a substantial and persistent challenge for genomic medicine due to their marked clinical and genetic heterogeneity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the transformative impact of next-generation sequencing (NGS) on rare disease diagnostics, a considerable proportion of patients remain undiagnosed following initial testing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In many cases, the failure to achieve a diagnosis reflects interpretive limitations rather than the true absence of a genetic cause, as previously \u0026ldquo;negative\u0026rdquo; genomic findings may become interpretable as knowledge and analytical frameworks evolve [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Variant interpretation is inherently dynamic, shaped by ongoing advances in genotype-phenotype understanding, expansion of population reference databases, refinement of in silico prediction tools, and improvements in bioinformatic pipelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In parallel, incomplete phenotypic characterization at the time of initial testing may further hinder accurate genotype-phenotype correlation, leading to missed or deprioritized candidate variants [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSystematic reanalysis has therefore emerged as an effective and cost-efficient strategy for solving undiagnosed rare diseases. By reinterpreting raw sequencing data using updated analytical pipelines, expanded genomic resources, and refined clinical information derived from deep phenotyping, reanalysis can substantially increase diagnostic yield without the need for immediate resequencing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, the success of reanalysis depends not only on technical advances but also on specialized expertise; clinicians and bioinformaticians with dedicated experience in rare disease genomics remain limited in number. Structured multidisciplinary frameworks that integrate clinicians from diverse medical specialties with bioinformatics expertise are thus critical for improving diagnostic outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to these needs, several countries have established Undiagnosed Diseases Programs (UDPs) that combine systematic phenotyping, harmonized genomic reanalysis, and multidisciplinary case review within structured workflows [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By centralizing clinical and bioinformatic expertise, these programs provide an effective framework for addressing complex, previously unsolved cases consistently demonstrating improved diagnostic outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Beyond their clinical impact, UDPs have contributed to the discovery of novel disease mechanisms and the expansion of genotype-phenotype associations. Nevertheless, while structured UDPs are increasingly adopted, real-world data remain limited, and experiences from middle-income healthcare systems are still underrepresented in the literature [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcibadem University Rare Diseases and Orphan Drugs Application and Research Center (ACURARE), established in 2017, serves as a national academic hub for rare disease research in Istanbul, the most populated city in T\u0026uuml;rkiye. Between 2021 and 2024, the İSTisNA platform was implemented as a structured capacity-building initiative aimed at establishing a coordinated ecosystem for undiagnosed and rare diseases by bringing together scientists, public institutions, patient organizations, and biobanks, while strengthening multidisciplinary evaluation, deep phenotyping, genomic analysis workflows, and workforce development. Building on this infrastructure, the Undiagnosed Diseases Program-Istanbul (UDP-IST) was launched in January 2024 as the first structured, multidisciplinary program in the country dedicated to patients with unsolved genetic disorders. In this study, we present the outcomes of UDP-IST, reporting two years of data from a cohort of 121 individuals with undiagnosed rare diseases. We evaluate the diagnostic impact of a standardized framework integrating deep phenotyping, systematic genomic reanalysis, and targeted resequencing, providing real-world evidence for the clinical value of structured undiagnosed disease programs.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProgram Overview\u003c/h2\u003e \u003cp\u003eThis study reports the experience of UDP-IST, a structured program established at ACURARE to evaluate patients with undiagnosed rare diseases through systematic phenotyping and genomic reanalysis (Fig.\u0026nbsp;1). The analysis includes data collected during the program\u0026rsquo;s first two years.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e The illustration summarizes the UDP-IST diagnostic workflow for referred patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReferral and Case Submission\u003c/h3\u003e\n\u003cp\u003ePatients who had undergone WES or WGS without a definitive molecular diagnosis were considered for evaluation. Referrals to UDP-IST were made by clinicians, including clinical geneticists, pediatricians, neurologists, and other subspecialties involved in clinical care. Submissions were made through an ACURARE-managed online portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://istudp.istisna.org/Auth?ReturnUrl=%2F\u003c/span\u003e\u003cspan address=\"https://istudp.istisna.org/Auth?ReturnUrl=%2F\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e enabling upload of clinical summaries, pedigree, laboratory, and imaging data, previous genetic reports, raw sequencing files (FASTQ, BAM, or VCF), and written informed consent authorizing genomic data review and secure biobanking.\u003c/p\u003e \u003cp\u003eEach unsolved case underwent an initial eligibility assessment by the UDP-IST coordination team. This eligibility assessment included review of informed consent forms, available clinical information, and evaluation of the technical quality of the uploaded genomic data to determine the feasibility of reanalysis. Cases meeting eligibility criteria proceeded to structured deep phenotyping and standardized genomic reanalysis performed using the in-house GenNext bioinformatics pipeline within the UDP-IST workflow (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAll submitted clinical documentation, genomic datasets, and consent forms were archived within the Acibadem University Biobank, which operates under institutional governance and national regulatory requirements. Biospecimens and associated data are stored in monitored, access-controlled facilities to ensure secure, compliant, and traceable long-term preservation.\u003c/p\u003e\n\u003ch3\u003eDeep Phenotyping\u003c/h3\u003e\n\u003cp\u003eAll eligible unsolved cases underwent a structured deep phenotyping process by the UDP-IST clinical team led by two experienced clinical geneticists (OAD and YA) who are active members of the UDNI-DWG (Undiagnosed Disease Network International-Diagnostic Working Group). Initial case discussions were conducted via online meetings with the referring clinician, during which medical history, symptom evolution, prior evaluations, and ambiguous or incomplete clinical features were systematically reviewed. Particular attention was given to resolving inconsistencies and capturing subtle but clinically relevant findings that may have been underrepresented in referral documentation. This collaborative review enabled the UDP-IST clinical team to develop a comprehensive understanding of the patient\u0026rsquo;s clinical presentation prior to phenotypic annotation.\u003c/p\u003e \u003cp\u003eFollowing this review, clinical features were systematically encoded using standardized Human Phenotype Ontology (HPO) terminology. This approach generated a comprehensive, up-to-date phenotypic profile for each patient, which formed the basis for downstream genomic interpretation.\u003c/p\u003e\n\u003ch3\u003eGenomic Data Reanalysis, and Resequencing\u003c/h3\u003e\n\u003cp\u003eAll available genomic data was reanalyzed using ACURARE\u0026rsquo;s in-house bioinformatics pipeline, GenNext workflow (Geniva Informatics; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/GenivaInformatics/gennext-workflows/tree/main/workflows\u003c/span\u003e\u003cspan address=\"https://github.com/GenivaInformatics/gennext-workflows/tree/main/workflows\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Raw sequencing data (FASTQ), alignment files (BAM), or variant call files (VCF) were reprocessed through a unified pipeline to ensure analytical consistency across datasets.\u003c/p\u003e \u003cp\u003eVariant annotation incorporated population frequency databases (gnomAD) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], disease databases (OMIM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], ClinVar [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], HGMD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]), and in silico prediction tools including CADD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], REVEL [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], VAMPP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and SpliceAI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Expanded splice-site analysis and copy-number variant (CNV) screening were performed when technically feasible. All variants were evaluated in the context of the refined HPO-based clinical phenotype derived from deep phenotyping and classified according to ACMG/AMP criteria [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll genomic and clinical findings were reviewed in multidisciplinary, case-based online meetings involving clinical geneticists, the primary clinician responsible for the patient\u0026rsquo;s follow-up, relevant subspecialists from other clinical disciplines, and bioinformaticians. These meetings served as the central decision-making platform for integrating refined phenotypic information with genomic findings and determining subsequent diagnostic steps. Resequencing was performed when additional analysis was considered necessary after multidisciplinary review. In selected families, this included expanding prior singleton WES to trio or quad analyses by incorporating additional family members to support variant interpretation and segregation analysis. In cases where previous WES data were technically inadequate, due to insufficient coverage, low read depth, or poor overall data quality, a new WES or WGS analysis was performed.\u003c/p\u003e\n\u003ch3\u003eDiagnostic Classification and Reporting\u003c/h3\u003e\n\u003cp\u003eCases were classified as solved or unsolved based on multidisciplinary consensus. Solved cases included patients with pathogenic (P) or likely pathogenic (LP) variants, as well as those with variants of uncertain significance (VUS) considered \u0026ldquo;clinically relevant\u0026rdquo; with genotype-phenotype correlation. Unsolved cases comprised patients with negative findings or variants deemed unrelated to the clinical presentation.\u003c/p\u003e \u003cp\u003eFor all cases, solved or unsolved, a technical report summarizing the genomic analysis and its clinical interpretation was generated and returned to the referring clinician. When additional clarification or counseling was required, patients and families were invited to the ACURARE UDP-IST Experience Center for in-person visits. Genetic counseling was provided focusing on interpretation of the results, clinical implications, and potential follow-up or reanalysis strategies. Patients who remained undiagnosed/unsolved after completion of the UDP-IST workflow were considered candidates for referral to UDNI-DWG. Potential data-sharing and referral processes were discussed with families.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCohort Characteristics\u003c/h2\u003e \u003cp\u003eBetween January 2024 and October 2025, 121 patients were consecutively evaluated within the UDP-IST workflow (Fig.\u0026nbsp;1). The cohort included 48 females (39.7%) and 73 males (60.3%), with a mean age at first referral of 7.44 years (SD 6.61; 95% CI 6.25\u0026ndash;8.63) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most patients were referred during childhood, with 92,6% evaluated before 18 years of age; the largest age group was 5\u0026ndash;12 years (Fig.\u0026nbsp;2a). The majority of individuals were of Turkish nationality (113/121, 93.4%), while 8 patients (6.6%) were international referrals. Parental consanguinity was reported in 38 families (31.4%), and family history suggestive of inherited disease was documented in 28.1% of cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean interval between initial WES/WGS analysis to reanalysis within UDP-IST was 21.7 months (SD 18.85; 95% CI 18.18\u0026ndash;25.17).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic and clinical characteristics of the UDP-IST cohort\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39,67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60,33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first referral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27,27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;12\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93,39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsanguinity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68,60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAffected Family Member\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71,90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Phenotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68,60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDysmorphism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune-system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInborn Errors of Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Genetic Testing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBroad-range NGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"10\" rowspan=\"11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWES\u0026thinsp;+\u0026thinsp;WGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle Gene Testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene Panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReanalysis Test Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69,42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuadro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResequencing Test Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuadro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75,00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Age distribution and expansion of phenotypic annotation through deep phenotyping in UDP-IST cohort. (a) Age distribution of patients at first referral to the UDP-IST. (b) Mean number of Human Phenotype Ontology (HPO) terms at initial genetic testing and at reanalysis, demonstrating a marked increase in phenotypic annotation following deep phenotyping.\u003c/p\u003e \u003cp\u003eNeurodevelopmental disorders (NDD) were the most common indication for referral, accounting for 83 patients (68.6%). The remaining 38 cases (31.4%) comprised a heterogeneous group, most frequently involving musculoskeletal disorders, along with other system-related conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior genetic testing other than WES or WGS had been performed in 63 patients (52.1%), most commonly chromosomal microarray analysis and karyotyping, followed by gene panel testing and single-gene analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes patient demographics, primary phenotypes, prior genetic testing, and genomic testing approaches in the UDP-IST cohort.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDeep-phenotyping and Reanalysis/Resequencing\u003c/h3\u003e\n\u003cp\u003eDeep phenotyping resulted in a marked increase in phenotypic annotation. The mean number of HPO terms per patient increased from 6.6 (SD 7.01; 95% CI 5.32\u0026ndash;7.96), based on information documented in the initial genetic testing reports, to 13.6 (SD 7.67; 95% CI 12.26\u0026ndash;15.03) following evaluation within the UDP-IST framework, corresponding to an absolute increase of 7 HPO terms and an approximate two-fold enrichment of phenotypic detail (Fig.\u0026nbsp;2b).\u003c/p\u003e \u003cp\u003eGenomic reanalysis was most frequently performed using singleton WES (84/121, 69.4%), with trio (5/121, 4.1%) and quad (4/121, 3.3%) designs applied in selected cases. WGS reanalysis was performed in 28 patients (23.1%), predominantly as singleton analyses. Resequencing was performed in a subset of patients, including WES in 2 cases (1.7%) and WGS in 17 cases (14.0%), with family-based designs (trio, or quad) used when indicated to support variant discovery and interpretation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the datasets accepted for reanalysis, most consisted of raw sequencing data: 115 of 121 cases (95.0%) were submitted as FASTQ files, 5 as VCF files, and 1 as a BAM file. Most datasets were generated in 2021 or later (94/121, 77.7%), with the remainder generated between 2015 and 2020 (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 3\u003c/strong\u003e \u003cp\u003eDistribution of raw sequencing data creation year by diagnostic outcome in reanalyzed cases. The majority of reanalysis-based diagnoses were observed among cases with sequencing data generated after 2021, coinciding with improvements in sequencing quality, coverage, and interpretative pipelines over time.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Outcomes\u003c/h2\u003e \u003cp\u003eWithin the UDP-IST framework, a definitive or clinically relevant genetic diagnosis was established in 46 of 121 patients, corresponding to an overall diagnostic yield of 38.0% (Fig.\u0026nbsp;4). Among the solved cases, 23 patients (19.0%) harbored P/LP variants, and 23 (19.0%) had VUSs considered clinically relevant based on multidisciplinary evaluation and genotype-phenotype correlation. The remaining patients (62.0%) were classified as unsolved, as no variant explaining the clinical phenotype was identified (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eMost diagnoses were achieved through reanalysis, which solved 40 of 46 cases (87.0%), whereas 6 cases (13.0%) were solved following resequencing. Among reanalysis-based diagnoses, WES was the predominant modality (34 cases, 85.0%), followed by WGS (6 cases, 15.0%). In the resequencing group, diagnoses were obtained using both WES and WGS. Two cases were solved by family-based WES (one trio (P36) and one quad (P1)), while the remaining four cases were resolved by solo whole-genome sequencing (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eThe time interval between the initial genetic test and reanalysis slightly differed between the solved and unsolved groups. Among patients undergoing reanalysis, the mean interval between the initial genetic test and reanalysis was longer in the solved group (22.83 months; median, 18 months, CI 95% [16.20-29.47]) than in the unsolved group (20,82 months; median, 13 months, CI 95% [16.34\u0026ndash;25.30]. Although this difference did not reach statistical significance, it indicates a trend toward diagnostic success being observed after a longer interval, approximately 1.5 to 2 years, between the initial test and reanalysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4.\u003c/b\u003e Diagnostic outcomes and contribution of reanalysis and resequencing within the UDP-IST cohort.\u003c/p\u003e \u003cp\u003eThe figure summarizes the overall diagnostic yield and the relative contribution of genomic reanalysis and resequencing strategies to diagnosis among the 121 patients evaluated within the UDP-IST framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhenotype-Specific Diagnostic Yield and Variant Characteristics\u003c/h2\u003e \u003cp\u003eDiagnostic yield varied across phenotypic categories. Among patients with NDD, 30 of 83 (36.1%) were solved. Higher proportional yields were observed in selected non-NDD groups, most notably among patients with musculoskeletal phenotypes. No diagnoses were established among patients referred primarily for dysmorphism features, metabolic, or vascular phenotypes (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5.\u003c/b\u003e Phenotype-specific diagnostic yield in the UDP-IST cohort. (IEM*: Inborn errors of metabolism). Distribution of primary phenotypic categories and corresponding diagnostic outcomes (solved vs. unsolved) among 121 patients, with gene-level summary of diagnosed cases shown on the right.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInheritance Patterns and Variant Characteristics\u003c/h2\u003e \u003cp\u003eIn solved cases, autosomal dominant inheritance accounted for 81.3% of diagnoses, followed by autosomal recessive inheritance in 18.7% (Fig.\u0026nbsp;6a). Cases involving candidate genes without established gene-phenotype associations were excluded from inheritance analyses.\u003c/p\u003e \u003cp\u003eAcross 46 solved patients, 51 clinically relevant variants in 45 genes were identified, with missense variants predominating at 80.4%. Truncating variants, including frameshift and nonsense variants, collectively accounted for 11.8%, while copy-number variants, splice-site variants, repeat expansions, and non-coding variants each represented 7.8% of variants (Fig.\u0026nbsp;6b). Twenty five of the identified variants were novel, including 10 classified as pathogenic or likely pathogenic and 15 classified as variants of uncertain significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 6.\u003c/b\u003e Inheritance patterns and variant characteristics among solved cases in the UDP-IST cohort. \u003cb\u003e(a)\u003c/b\u003e Distribution of inheritance patterns among solved patients, with autosomal dominant diagnoses predominating, followed by autosomal recessive. Cases involving candidate genes without established gene-phenotype associations were excluded from inheritance analysis. \u003cb\u003e(b)\u003c/b\u003e Distribution of clinically relevant variant types identified in solved cases, stratified by zygosity. Missense variants constituted the majority, followed by loss-of-function variants, while splice-site variants, copy-number variants, repeat expansions, and non-coding variants were observed less frequently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReanalysis-based Diagnoses\u003c/h2\u003e \u003cp\u003eReanalysis emerged as the primary driver of diagnostic resolution, accounting for 87.0% of solved cases. Two complementary factors underpinned diagnostic success within this group. In a subset of patients (7/40, 17.5%), diagnostic variants were identified in genes for which gene-phenotype associations or relevant genotype-phenotype correlations became established after initial genetic testing, enabling diagnosis through updated gene-disease knowledge.\u003c/p\u003e \u003cp\u003eIn the remaining reanalysis-based diagnoses, causative variants were identified in genes that were already recognized as disease-associated at the time of the initial genetic testing. In these cases, diagnostic resolution was primarily supported by improved phenotype-genotype alignment achieved through systematic deep phenotyping, reflected by an increased number of HPO terms incorporated prior to reanalysis. This refinement of phenotypic information directly facilitated variant prioritization and strengthened clinical interpretation.\u003c/p\u003e \u003cp\u003eDetails of the variants identified in patients diagnosed through reanalysis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All HPO terms used for phenotypic annotation and genotype-phenotype correlation analyses in solved cases are summarized in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular and phenotypic characteristics of variants identified in solved cases through genomic reanalysis and resequencing in the UDP-IST cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranscript ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariant Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAmino Acid Change\u003c/p\u003e \u003cp\u003e(if applicable)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZygosity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDiagnosis*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMain Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eDiagnosis Through Reanalysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCACNA1S\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNM_000069.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.4081T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.Phe1361Ile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeterozygous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelevant VUS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHypokalemic periodic\u003c/p\u003e \u003cp\u003eparalysis, type 1\u003c/p\u003e \u003cp\u003e(#170400)\u003c/p\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNDD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOL4A2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001846.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.4987G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gly1663Ser\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBrain small vessel disease 2A, autosomal dominant\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#614483)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWDR62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001083961.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.3936dup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTruncating Frameshift\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Val1313ArgfsTer18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMicrocephaly 2, primary, autosomal recessive, with or without cortical malformations\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#604317)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP16ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSUZ12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_015355.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.167C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Ser56Phe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eImagawa-Matsumoto syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#618786)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eKCNH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_172362.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1486G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gly496Arg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTemple-Baraitser syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#611816)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP26ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTNPO2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001382241.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.191C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Ser64Phe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eIntellectual developmental disorder with hypotonia, impaired speech, and dysmorphic facies\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619556)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP27ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOL12A1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_004370.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1337A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Asp446Ala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBethlem myopathy 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#616471)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGFI1B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001377304.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.569G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg190Gln\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBleeding disorder,\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eplatelet-type, 17\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#187900)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eHematological\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP29ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eB3GALT6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNM_080605.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.235A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Thr79Ala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCompound Heterozygous (in trans)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSpondyloepimetaphyseal dysplasia with joint laxity, type 1, with or without fractures\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#271640)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.467A\u0026gt;Cᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Asp156Ala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP31ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCHD3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001005273.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2973G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Met991Ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSnijders Blok-Campeau syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#618205)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP32**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHPDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_032756.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.149G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gly50Asp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpastic paraplegia 83, autosomal recessive\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619027)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP34ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLFNG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001040167.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1073G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg358Lys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpondylocostal dysostosis 3, autosomal recessive\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#609813)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFBXO11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001190274.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.412A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg138Gly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eIntellectual developmental disorder with dysmorphic facies and behavioral abnormalities\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#618089)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNovel Gene Candidate (Unpublished Results)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNovel Gene Candidate (Unpublished Results)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNovel Gene Candidate (Unpublished Results)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP53ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eATP9A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_006045.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.817G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Val273Ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNeurodevelopmental disorder with poor growth and behavioral abnormalities\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#620242)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOPA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_004371.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.3533G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg1178His\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAutoinflammation and autoimmunity, systemic, with immune dysregulation 1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#616414)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eImmune-system\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP55ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eITPR3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_002224.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.812A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gln271Arg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eCharcot-Marie-Tooth disease, demyelinating, type 1J\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#620111)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOL12A1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_004370.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.4418-1G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSplice Site\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBethlem myopathy 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#616471)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP63ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGO2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_012154.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1805C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Pro602His\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eLessel-Kreienkamp syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619149)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePOGZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_015100.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2989C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNonsense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg997Ter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWhite-Sutton syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#616364)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eGastrointestinal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP68**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSMARCA2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001289396.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1514G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg505Gln\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBlepharophimosis-impaired intellectual development syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619293)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP72**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eKMT2C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_170606.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1436A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gln479Pro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eKleefstra syndrome 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#617768)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP74ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHCN1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_021072.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1253T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Met418Thr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eDevelopmental and epileptic encephalopathy 24\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#615871)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP75**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOL6A3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_004369.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.7024C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNonsense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg2342Ter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eUllrich congenital muscular dystrophy 1C\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#620728)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD, AR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNEB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001164508.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.25183C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNonsense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg8395Ter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNemaline myopathy 2, autosomal recessive\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#256030)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eASXL3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_030632.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2471C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Pro824Leu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBainbridge-Ropers syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#615485)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP85ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePOGZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_015100.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2571G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg857Ser\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWhite-Sutton syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#616364)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP88ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTUBA1A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_006009.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.514T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Tyr172His\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eLissencephaly 3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#611603)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP90**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSCN1A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001165963.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.4769T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Leu1590Pro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eDevelopmental and epileptic encephalopathy 6B, non-Dravet\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619317)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eKIF7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNM_198525.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2272G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Glu758Lys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCompound Heterozygous (in trans)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAcrocallosal syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#200990)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.259A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Asn87Asp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP98ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eADNP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001282531.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1811C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Pro604Leu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHelsmoortel-van der Aa syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#615873)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP100ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCACNB2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_201596.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1421C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Pro474Arg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBrugada syndrome 4\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#611876)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP102ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCACNA1G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_018896.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1284G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Glu428Asp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpinocerebellar ataxia 42, early-onset, severe, with neurodevelopmental deficits\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#618087)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP103\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSMO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_005631.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1285A\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Ile429Phe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ePallister-Hall-like syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#241800)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP111ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGNB2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_005273.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.233A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Lys78Arg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSick sinus syndrome 4\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619464)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP113ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFXN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_000144.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2T\u0026thinsp;\u0026gt;\u0026thinsp;Gᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Met1Arg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCompound Heterozygous (in trans)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFriedreich ataxia\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#229300)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_000144.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.165\u0026thinsp;+\u0026thinsp;1340 GAA\u003c/b\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003cb\u003eGAA[\u0026gt;\u0026thinsp;219]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRepeat expansion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP116ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eASH1L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_018489.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.8470G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Val2824Ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eIntellectual developmental disorder, autosomal dominant 52\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#617796)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP118ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVPS4A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_013245.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.746C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Thr249Lys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eCIMDAG syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619273)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP119ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTCOF1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001371623.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2429del\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTruncating Frameshift\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Gly810GlufsTer18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTreacher Collins syndrome 1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#154500)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis Through Resequencing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP1ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCFAP410\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_004928.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.322C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg108Cys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpondylometaphyseal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003edysplasia, axial\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#602271)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOcular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP12ᵇ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRAP1B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_001010942.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.-27\u0026thinsp;+\u0026thinsp;2T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Tyr324Cys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eThrombocytopenia 11 with multiple congenital anomalies and dysmorphic facies\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#620654)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP36ᵇ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNOTCH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_017617.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.4787T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Leu1596Pro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHeterozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ePMID: 38778082\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRMRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNR_003051.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en.94dup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNoncoding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMetaphyseal dysplasia without\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ehypotrichosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#250460)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP69ᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOGDHL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNM_018245.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1826G\u0026gt;Tᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Ser609Ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCompound Heterozygous (in trans)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRelevant VUS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eYoon-Bellen neurodevelopmental syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619701)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.2026G\u0026gt;Aᵃ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Val676Ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNM_024911.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ec.1261C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep.Arg421Trp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eHomozygous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLP/P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eZaki syndrome\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(#619648)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e* Diagnoses are classified according to OMIM phenotypes.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e** Indicates cases in which diagnostic variants were identified in genes for which gene\u0026ndash;phenotype associations or relevant genotype\u0026ndash;phenotype correlations became established only after the initial testing.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eᵃ Indicates cases in which a novel variant was identified.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eᵇ Indicates cases in which diagnostic variants were identified in genes for which gene\u0026ndash;phenotype associations or relevant genotype\u0026ndash;phenotype correlations became established only after the initial testing and in which a novel variant was identified.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eResequencing-based Diagnoses\u003c/h2\u003e \u003cp\u003eFurther diagnostic testing, including trio or quad WES, WGS, and RNA sequencing, was performed in 20 patients who remained undiagnosed after reanalysis. Through these resequencing approaches, an additional six patients achieved a molecular diagnosis.\u003c/p\u003e \u003cp\u003eIn patients undergoing trio or quad WES, a molecular diagnosis was achieved in all cases. In one patient (P36), the causative variant had been present in the reanalysis dataset but could not be interpreted as diagnostic at the time, as the relevant phenotypic expansion of the implicated gene had not yet been described in the literature. An updated literature search enabled recognition of the variant as causative following resequencing. In another patient (P1), the missed diagnosis was attributed to insufficient exon-level coverage in the initial reanalysis. The causative variant was present at low coverage, failed quality filtering thresholds, and was therefore filtered during reanalysis. Resequencing achieved adequate coverage of the relevant genomic region, allowing reliable detection and interpretation of the variant.\u003c/p\u003e \u003cp\u003eWGS led to diagnostic findings in four patients whose causative variants could not be detected through reanalysis due to multiple underlying factors. In the first patient (P69), the diagnosis was established in a gene for which the disease association had been reported after the reanalysis. In the second patient (P61), a pathogenic variant was identified in a long non-coding RNA gene (\u003cem\u003eRMRP\u003c/em\u003e) that had not been captured by prior WES analysis. In the remaining two patients (P12 and P73), technical limitations of the initial analyses, restricted coverage and reliance on VCF-only data, precluded detection of the causative variants during reanalysis.\u003c/p\u003e \u003cp\u003eDetails of the variants identified in patients diagnosed through resequencing are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn three patients (Patients 44, 48, and 51), variants were identified in candidate genes that have not previously been associated with the observed clinical phenotypes. These findings represent putative novel gene-phenotype associations, and further evaluation of the pathogenic relevance of the identified variants is ongoing through collaborative efforts and functional studies.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes molecular findings in solved cases following genomic reanalysis or resequencing, including variant details, inheritance, associated OMIM diagnosis, and primary phenotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSelected Cases\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eDiagnosis by Reanalysis Through Emerging Gene-Phenotype Associations, P38\u003c/h2\u003e \u003cp\u003eWe report a 6-year-old female patient presenting with global developmental delay, behavioral abnormalities, dysmorphic facial features, and multisystem involvement. She was born at term to non-consanguineous, healthy parents. Developmental milestones were delayed, with marked speech impairment, autistic features, hyperactivity, bruxism, and poor attention. Physical examination revealed microcephaly, a high and prominent forehead, high palate, ptosis, sparse hair, congenital bilateral hip dislocation, cutaneous syndactyly of the toes, and hiatus hernia.\u003c/p\u003e \u003cp\u003eInitial genetic analyses, including chromosomal microarray analysis and WES, were inconclusive. The WES FASTQ data were reanalyzed four years after the initial testing. Reanalysis identified a pathogenic, heterozygous, de novo, missense variant in \u003cem\u003eFBXO11\u003c/em\u003e (NM_001190274.2:c.412A\u0026thinsp;\u0026gt;\u0026thinsp;G; p.Arg138Gly). Reverse phenotyping demonstrated strong concordance between the patient\u0026rsquo;s clinical features and \u003cem\u003eFBXO11\u003c/em\u003e-related intellectual developmental disorder with dysmorphic facies and behavioral abnormalities (OMIM #618089), leading to a definitive molecular diagnosis. The initial WES was reported in June 2018, before \u003cem\u003eFBXO11\u003c/em\u003e was established as a morbid OMIM gene, precluding clinical interpretation at that time; the gene-phenotype association was reported shortly thereafter, in August 2018.\u003c/p\u003e \u003cp\u003eThis case highlights the importance of integrating comprehensive and up-to-date literature evidence beyond curated databases such as OMIM in the setting of rapidly evolving gene-phenotype knowledge.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVCF-Only Reanalysis Masking\u003c/b\u003e \u003cb\u003eCFAP410\u003c/b\u003e\u003cb\u003e-Related Skeletal Dysplasia, P1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe report a 20-year-old male patient presenting with optic atrophy, cone-rod dystrophy, strabismus, scoliosis, short stature, and brachydactyly, consistent with a multisystem skeletal dysplasia with ocular involvement. Parental consanguinity was denied.\u003c/p\u003e \u003cp\u003ePrevious genetic tests, including chromosomal analysis, chromosomal microarray analysis, and WES, were inconclusive. When reanalysis was planned within the UDP-IST framework, only VCF-format data were provided. Reanalysis based on VCF data did not identify a causative variant. Subsequently, quadro-based WES resequencing was performed, which identified an LP, homozygous missense variant in \u003cem\u003eCFAP410\u003c/em\u003e (NM_004928.3:c.322C\u0026thinsp;\u0026gt;\u0026thinsp;T; p.Arg108Cys). Reverse phenotyping demonstrated strong concordance between the patient\u0026rsquo;s clinical features and \u003cem\u003eCFAP410\u003c/em\u003e-related axial spondylometaphyseal dysplasia (OMIM #602271), leading to a definitive molecular diagnosis.\u003c/p\u003e \u003cp\u003eThis case highlights the diagnostic limitations of VCF-only reanalysis and underscores the critical importance of access to raw sequencing data, as the absence of raw files precludes reassessment of read-level evidence and coverage metrics, allowing pathogenic variants filtered during initial analyses to remain undetected.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNon-coding RNA Genes as a Diagnostic Consideration in WES-Negative Patients, P61\u003c/h2\u003e \u003cp\u003eWe report an 8-year-old male patient presenting with disproportionate short stature, mild intellectual disability, and skeletal dysplasia. He was born at term to consanguineous parents, with prenatal ultrasonography showing shortened long bones. Developmental delay and characteristic skeletal features, including rhizomelia, brachydactyly, genu varum, and a waddling gait, were noted, along with recurrent infections and transient liver enzyme elevation.\u003c/p\u003e \u003cp\u003ePrevious genetic investigations, including chromosomal analysis, chromosomal microarray, mitochondrial DNA analysis, and WES, were inconclusive. Reanalysis of WES FASTQ data within the UDP-IST framework did not identify a causative variant. Subsequent WGS identified a homozygous insertion variant in the non-coding RNA gene \u003cem\u003eRMRP\u003c/em\u003e (NR_003051.4:n.94dup), classified as likely pathogenic. Reverse phenotyping demonstrated strong concordance with \u003cem\u003eRMRP\u003c/em\u003e\u003cb\u003e-\u003c/b\u003erelated disorders, including cartilage-hair hypoplasia, leading to a definitive molecular diagnosis.\u003c/p\u003e \u003cp\u003eThis case highlights the limitations of exome-based approaches for detecting pathogenic variants in non-coding RNA genes and underscores the added diagnostic value of genome-wide sequencing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReanalysis Revealing a Hidden Second Allele in\u003c/b\u003e \u003cb\u003eFXN\u003c/b\u003e, \u003cb\u003eP113\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe report an adolescent male patient presenting with progressive gait instability, cerebellar ataxia, dysmetria, dysdiadochokinesis, fatigue, and mild scoliosis. Early development was unremarkable; neurological symptoms evolved gradually during adolescence. Neurological examination and cranial MRI findings were consistent with spinocerebellar ataxia.\u003c/p\u003e \u003cp\u003eInitial diagnostic evaluation included WGS, which did not identify a causative variant; subsequent targeted repeat expansion testing for common spinocerebellar ataxia subtypes was also negative. Friedreich ataxia-specific testing subsequently revealed a heterozygous GAA repeat expansion in the \u003cem\u003eFXN\u003c/em\u003e gene, inherited from the mother. As no second pathogenic allele was identified at that time, this finding was interpreted as a carrier state.\u003c/p\u003e \u003cp\u003eReanalysis of the WGS data within the UDP-IST framework identified a heterozygous start-loss variant in \u003cem\u003eFXN\u003c/em\u003e (c.2T\u0026thinsp;\u0026gt;\u0026thinsp;G; p.Met1Arg), inherited from the father. The presence of the GAA repeat expansion in trans with the start-loss variant established compound heterozygosity, leading to a definitive diagnosis of Friedreich ataxia. Reverse phenotyping confirmed concordance between the patient\u0026rsquo;s clinical features and the known \u003cem\u003eFXN\u003c/em\u003e-related disease spectrum.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we present two-years experience of the Undiagnosed Diseases Program-Istanbul, demonstrating the clinical impact of a structured, clinician-led, multidisciplinary framework that integrates deep phenotyping with systematic genomic reanalysis. Using this approach, a genetic diagnosis was established in 38% of a highly complex cohort of patients with previously unsolved rare diseases, despite extensive prior genetic testing and predominant use of singleton sequencing. This diagnostic yield places UDP-IST within the upper range reported by established international programs, including the NIH Undiagnosed Diseases Network (UDN), the Australian Adult UDP (AHA-UDP), and national initiatives from Europe and Asia, where yields typically range from 25% to 45% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Observed variability across programs likely reflects differences in cohort composition, referral pathways, availability of family-based data, and analytical strategies rather than intrinsic differences in program performance.\u003c/p\u003e \u003cp\u003eIn UDP-IST, a clinician-led referral model is employed, whereby case submission is initiated by the primary clinician rather than directly by patients or families. While patient-initiated application models used by some international programs may enhance accessibility, they may also increase the burden on limited expert resources, particularly in healthcare systems with constrained specialist capacity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The clinician-led model adopted in UDP-IST facilitates focused triage, ensures that referrals are grounded in a strong suspicion of Mendelian disease, and enables more efficient allocation of genomic and bioinformatic expertise\u0026mdash;an important consideration in middle-income healthcare systems such as T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eA key observation of this study is that genomic reanalysis accounted for nearly three-quarters of all solved cases, underscoring its central role in diagnostic resolution. This proportion is comparable to, and in some instances exceeds, those reported by other undiagnosed disease programs, where reanalysis contributes to approximately 40\u0026ndash;60% of diagnoses depending on cohort characteristics and analytical frameworks [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Taken together, these findings reinforce accumulating evidence that many initially negative or inconclusive genomic results reflect interpretive and contextual limitations rather than the true absence of a genetic etiology [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In line with this growing body of evidence, reanalysis success in UDP-IST was primarily driven by improved phenotype-genotype correlation achieved through deep phenotyping, the emergence of new gene-disease associations, and advances in bioinformatic pipelines.\u003c/p\u003e \u003cp\u003eDeep phenotyping emerged as a critical contributor to diagnostic success in UDP-IST. Beyond structured data annotation, this process relied on iterative clinician-to-clinician dialogue, allowing refinement of subtle, evolving, or previously underrecognized clinical features. Notably, the central role of deep phenotyping in achieving high diagnostic yield has already been demonstrated by our group in a prior clinical whole-genome sequencing study, where systematic pre- and post-test phenotypic reassessment substantially enhanced variant interpretation and diagnostic yield [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This approach resulted in a marked increase in the number of HPO terms incorporated at reanalysis, highlighting the limitations of relying solely on phenotypic information captured at the time of initial genetic testing. Comparable benefits of systematic phenotypic reassessment have been reported across multiple undiagnosed disease programs, supporting its central role in enhancing diagnostic yield [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, all reanalysis-based diagnoses in UDP-IST were achieved in cases with access to raw FASTQ data, whereas reanalysis restricted to BAM or VCF files did not yield diagnostic findings. The inability to reassess coverage metrics and read-level evidence when raw data are unavailable represents a major limitation of VCF/BAM-only reinterpretation, increasing the likelihood that pathogenic variants filtered during initial analyses remain undetected. Similar observations have been reported in other cohorts, supporting the notion that when raw sequencing data are unavailable, resequencing may be a more effective diagnostic strategy than repeated reanalysis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough reanalysis was the principal driver of diagnostic resolution, resequencing provided additional diagnostic value in selected cases. Repeat whole-exome sequencing was required in some patients to address limitations of the initial data, including low coverage or incomplete datasets, enabling reliable variant detection. In other cases, whole-genome sequencing extended the diagnostic scope beyond exome-based approaches, allowing identification of pathogenic variants in non-coding regions, poorly captured exons, or repeat-associated mechanisms. Together, these findings highlight the complementary role of repeat WES and WGS in specific clinical contexts, while underscoring ongoing considerations regarding the routine first-line use of genome-wide sequencing given cost, infrastructure, and interpretive demands [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond individual diagnoses, structured UDPs have broader implications for healthcare systems and national rare-disease strategies. Genomic diagnoses achieved within UDP frameworks have been shown to reduce unnecessary investigations, shorten prolonged diagnostic odysseys, and inform targeted clinical management, collectively improving patient outcomes and cost-effectiveness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this regard, the UDP-IST model represents a pragmatic and scalable strategy for embedding genomic medicine into routine care and national rare-disease infrastructures, particularly in middle-income healthcare settings where resources must be carefully prioritized.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, the early experience of UDP-IST demonstrates that a structured, multidisciplinary approach integrating deep phenotyping with systematic genomic reanalysis can yield substantial diagnostic benefit for patients with undiagnosed rare diseases. By emphasizing interpretation, phenotypic refinement, and coordinated clinical-bioinformatic expertise rather than immediate technological escalation, UDP-IST offers a scalable model with relevance beyond national boundaries. Importantly, our findings align with and extend current recommendations supporting periodic genomic reanalysis as a core component of longitudinal care for patients with suspected genetic disorders, particularly once sufficient time has elapsed for knowledge and analytical frameworks to evolve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this broader context, these findings underscore the transformative potential of the UDP-IST framework to inform clinical practice, optimize resource utilization, and shorten diagnostic odysseys, while providing a robust and evidence-based foundation for the future development of a coordinated national undiagnosed diseases network. Future integration of genome-wide and transcriptomic approaches for unresolved cases, coupled with international data-sharing and national registry development, will be critical to maximizing both clinical and translational impact.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Acibadem Mehmet Ali Aydinlar University (ATADEK: 2023-14/511). Written informed consent was obtained from patients and/or families to share and publish clinical and genetic information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request, in accordance with ethical approvals and applicable data protection regulations.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll other authors declare no competing interests or conflicts of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the ISTisNA Project, funded by the Republic of T\u0026uuml;rkiye, Ministry of Industry and Technology, Istanbul Development Agency (ISTKA), Project No: TR10/19/FZD/0003. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOAD: Project administration, Genetic analysis, Writing-original draft. OO: Software, Writing-review and editing. GSOI: Data curation, Genetic analysis, Writing-review and editing. BY: Data curation, Formal analysis, Writing-review and editing. UO: Data curation, Formal analysis, Writing-review and editing. EA: Data curation and Genetic analysis. AB: Data curation and Genetic analysis. AY: Data curation and Genetic analysis. GO: Data curation and Genetic analysis. HG: Wet-lab Investigation (Sanger sequencing). BA: Data curation. JCY: Data curation. FK: Data curation. ZY: Data curation. EDA: Data curation. OHN: Project administration, Writing-review and editing. KB: Writing-review and editing. YA: Conceptualization, Project administration, Writing-original draft.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients and their families for their participation. We acknowledge the referring clinicians, collaborating clinicians, and medical students for their valuable contributions to this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOzlem Akgun Dogan \u0026ndash; [email protected] ORCID:0000-0002-8310-4053\u003c/p\u003e\n\u003cp\u003eOzkan Ozdemir\u003csup\u003e \u003c/sup\u003e\u0026ndash; [email protected] \u003c/p\u003e\n\u003cp\u003eGulsah Sebnem Ozkose Iyigel \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eBerkay Yildiz \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eUlas Ozonur \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eAybike S. Bulut \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eEylul Aydin \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eAyca Yigit \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eGizem Onder \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eHuma Gunay \u0026ndash; [email protected] \u003c/p\u003e\n\u003cp\u003eBeril Ay \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eJulide Ceren Yilmaz \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eFiruze Kokten \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eZeynep Yentur \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eEmiran Defne Atay \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eKaya Bilguvar \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eOzden Hatirnaz Ng \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003eYasemin Alanay \u0026ndash; [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoycott KM, et al. International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases. Am J Hum Genet. 2017;100(5):695\u0026ndash;705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaruscio D, et al. National registries of rare diseases in Europe: an overview of the current situation and experiences. Public Health Genomics. 2015;18(1):20\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSplinter K, et al. Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease. N Engl J Med. 2018;379(22):2131\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright CF, FitzPatrick DR, Firth HV. Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet. 2018;19(5):253\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarg N, et al. Reanalysis of Exome Sequencing Data in the Indian Undiagnosed Diseases Program: Improving Diagnostic Yield and Ending Diagnostic Odyssey. Clin Genet. 2025;107(6):620\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdedipe D, et al. Neurodevelopmental Phenotyping and Genotyping in the Pediatric National Institute of Health Undiagnosed Disease Program. Am J Med Genet B Neuropsychiatr Genet. 2025;198(8):230\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Y, et al. Accelerating rare disease detection: an experience of multidisciplinary team model in undiagnosed diseases program in a children's hospital. Front Public Health. 2024;12:1373649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallis M, et al. Experience of the first adult-focussed undiagnosed disease program in Australia (AHA-UDP): solving rare and puzzling genetic disorders is ageless. Orphanet J Rare Dis. 2024;19(1):288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlaba K, et al. Diagnostic efficacy and clinical utility of whole-exome sequencing in Czech pediatric patients with rare and undiagnosed diseases. Sci Rep. 2024;14(1):28780.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarczewski KJ, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKusick-Nathans Institute of Genetic, Medicine. J.H.U.B., MD), \u003cem\u003eOnline Mendelian Inheritance in Man, OMIM\u0026reg;\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandrum MJ, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStenson PD, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003;21(6):577\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRentzsch P, et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannidis NM, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99(4):877\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzdemir O et al. Molecular and In Silico Analysis of the CHEK2 Gene in Individuals with High Risk of Cancer Predisposition from Turkiye. Cancers (Basel), 2024. 16(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaganathan K, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535\u0026ndash;e54824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards S, et al. 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Genome Med. 2022;14(1):23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang G, et al. The national economic burden of rare disease in the United States in 2019. Orphanet J Rare Dis. 2022;17(1):163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller DT, et al. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23(8):1381\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep phenotyping, reanalysis, undiagnosed disease program","lastPublishedDoi":"10.21203/rs.3.rs-8842818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8842818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite transformative advances in next-generation sequencing, a large proportion of patients with suspected rare genetic disorders remain without a molecular diagnosis after initial testing. Increasing evidence indicates that this persistent diagnostic gap often reflects limitations in variant interpretation and phenotypic resolution rather than the true absence of a genetic etiology. Here, we report the two-year experience of the Undiagnosed Diseases Program-Istanbul (UDP-IST), a structured, clinician-led, multidisciplinary program integrating deep phenotyping with systematic genomic reanalysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe evaluated 121 individuals with previously unsolved rare diseases, achieving an overall diagnostic yield of 38%. The majority of diagnoses (87,0%) were obtained through genomic reanalysis alone, while resequencing provided additional diagnostic value in selected cases. Diagnostic success was driven by refined phenotype-genotype correlation, access to raw sequencing data, and evolving gene-disease knowledge.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings demonstrate the clinical value and scalability of structured genomic reanalysis frameworks and support periodic reanalysis as a core component of longitudinal care for patients with suspected genetic disorders, particularly in resource-constrained healthcare settings.\u003c/p\u003e","manuscriptTitle":"Undiagnosed Diseases Program-Istanbul (UDP-IST): Diagnostic Impact of Systematic Genomic Reanalysis with Deep Phenotyping in 121 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:55:31","doi":"10.21203/rs.3.rs-8842818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-28T13:29:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T13:15:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T17:43:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2026-02-12T13:42:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c3aa6f7-42bb-469f-85b1-9f3bfac0f8e3","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:55:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:55:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8842818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8842818","identity":"rs-8842818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}