Genetic testing plays a pivotal role in the diagnosis of neurological disorders, particularly those with early-onset, syndromic, or familial features. Diagnostic yields from such testing vary significantly depending on the clinical phenotype and the testing modality employed, typically ranging from 25% to 50% in both pediatric and adult neurogenetic clinics. In adult populations, next-generation sequencing (NGS) technologies have identified pathogenic variants in up to 30–40% of cases involving hereditary ataxias, neuropathies, and myopathies, influencing not only diagnostic accuracy but also therapeutic decisions and genetic counseling practices [1,2]. In India, the high prevalence of consanguineous marriages and the underrecognition of Mendelian neurological disorders highlight the urgent need for accessible, cost-effective, and comprehensive genetic diagnostics. However, data from low- and middle-income countries remain limited, especially in population subsets with complex clinical phenotypes and limited access to advanced sequencing platforms[3,4] Genetic testing is increasingly central to the diagnosis of neurological disorders, particularly those with early-onset, syndromic, or unexplained presentations. In this study, we aimed to assess the diagnostic yield of genetic testing in patients with suspected neurogenetic disorders presenting to a tertiary neurology referral center in South India and to evaluate the clinical utility of genetic findings in informing diagnosis, guiding clinical management, and facilitating genetic counseling.

This retrospective observational study was conducted at a tertiary care neurology center in Vijayawada, India, and included patients who underwent genetic testing between February 2023 and February 2025. Eligible participants were of any age and had a clinical suspicion of hereditary or unexplained neurological disorders that warranted genetic testing. Patients tested for non-neurological indications were excluded. Additional exclusion criteria included incomplete clinical or genetic data, such as the absence of confirmed test reports, missing clinical phenotype or demographic details, or the presence of incidental genetic findings not relevant to the neurological presentation. Clinical and genetic data were obtained from hospital medical records and laboratory reports. Information collected included patient age, sex, clinical diagnosis, and phenotypic classification (such as ataxia, epilepsy, or muscular dystrophy). The presence of parental consanguinity and a family history of neurological illness was documented. Details of the genetic testing modality were recorded, including whole exome sequencing, targeted gene panel testing, and repeat expansion analysis. Genetic findings were classified according to the American College of Medical Genetics and Genomics (ACMG) criteria into pathogenic, likely pathogenic, and variants of uncertain significance (VUS), with documentation of zygosity and inheritance pattern where applicable. Descriptive statistics were used to summarize demographic, clinical, and genetic characteristics. Categorical variables were expressed as frequencies and percentages, while continuous variables were reported as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on data distribution. Diagnostic yield was defined as the proportion of patients with pathogenic or likely pathogenic variants relative to the total number of cases tested, calculated as: Diagnostic Yield (%)=Number of P/LP cases Total cases tested×100\text{Diagnostic Yield (\%)} = \frac{\text{Number of P/LP cases}}{\text{Total cases tested}} \times 100Diagnostic Yield (%)=Total cases tested Number of P/LP cases×100 Associations between clinical and genetic variables and the diagnostic yield were assessed using Odds Ratios (OR) with 95% confidence intervals (CI). Fisher’s Exact Test was applied for categorical comparisons due to the small sample sizes in certain subgroups. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 30.0 (IBM Corp., Armonk, NY, USA). The study protocol was reviewed and approved by the Institutional Ethics Committee of the tertiary neurology center, and was conducted in accordance with the Declaration of Helsinki (2013).

Table 1: Demographic and Family History Characteristics of the Study Cohort (N = 32) Category Subgroup Number of Patients (n,%) Age Group < 18 years 19(59.37%) ≥ 18 years 12(37.50%) Sex Male 17(53.12%) Female 15(46.8%) Family History Positive 6(18.75%) Negative 26(81.25%) Consanguinity Present 6(18.75%) Absent 26(81.25%) Sex Male 17(53%) Female 15(47%) A total of 32 patients were included in the study. The age of participants ranged from 0.5 to 64 years, with a mean age of approximately 19.7 years. Pediatric patients (≤18 years) constituted 58% (n = 19) of the cohort, while adults (>18 years) accounted for 42% (n = 13). The gender distribution was relatively balanced, with 17 males (54%) and 15 females (46%). Table-2: Variant Classification Variant Category n (%) Pathogenic/Likely Pathogenic (P/LP) 12 (37%) Variants of Uncertain Significance 15 (47%) Negative (No Variant Found) 5 (16%) Most patients had Variants of Uncertain Significance (47%), followed by Pathogenic/Likely Pathogenic variants (37%), while 16% had no detectable variant. This shows that uncertain findings were most common, with over one-third having clinically significant variants. Table -3: Inheritance Patterns Inheritance Pattern Cases (n,%) Autosomal Recessive 18(58%) Autosomal Dominant 8(25%) X-linked Recessive 1(4%) No Variant Detected 5(16%) Most cases followed an autosomal recessive pattern (58%), with fewer autosomal dominant cases (25%). X-linked recessive inheritance was rare (4%), and 16% had no variant detected. Table-4: Demographic Factors and analysis Factor Odds Ratio (OR) p-Value Interpretation Consanguinity 5.00 0.153 ↑ Strong positive trend; borderline statistical significance Adult Age (>18y) 2.17 0.415 ↑ Higher diagnostic trend in adults Pediatric Age (≤18y) 0.63 0.683 ↓ Slightly lower diagnostic yield in pediatric group Female Sex 1.43 0.706 ↔ No significant sex-based difference Male Sex 0.89 1.000 ↔ Comparable to females Positive Family History 2.00 0.638 ↑ Mild positive association Consanguinity showed a strong positive trend toward higher diagnostic yield, though not statistically significant. Adults had a higher diagnostic trend than children. Sex and family history did not show significant associations with diagnostic yield. Table-5: Zygosity and Inheritance Patterns Factor Odds Ratio (OR) p-Value Interpretation Homozygous Variant 1.69 0.695 ↑ Frequent in consanguineous cases; suggests increased yield Heterozygous Variant 0.48 0.448 ↓ Less likely to be P/LP; biallelic variants more informative Autosomal Dominant 1.03 1.000 ↔ Not predictive of higher diagnostic yield Autosomal Recessive 0.82 1.000 ↔ Common inheritance but not independently predictive Homozygous variants were more frequent and suggested higher yield, particularly in consanguineous cases. Heterozygous variants were less likely to be pathogenic/likely pathogenic. Inheritance pattern alone (autosomal dominant or recessive) was not independently predictive. Table-6: Testing Modality and interpretation Modality Odds Ratio (OR) p-Value Interpretation Gene Panel Testing 3.21 0.326 ↑ Highest yield when phenotype-guided Repeat Expansion 1.88 0.613 ↑ Effective in select conditions like ataxias or HD Exome Sequencing 0.63 0.683 ↓ Lower yield compared to targeted testing in this cohort Phenotype-guided gene panel testing showed the highest diagnostic yield, followed by repeat expansion testing for selected conditions. Exome sequencing had a comparatively lower yield in this cohort. Table -7: Distribution of Clinical Phenotypes in the Study Cohort (N = 32) Phenotype Number of Patients (n,%) Neurodevelopmental Delay 6(19%) Hyperkinetic Movement Disorder 6(19%) Muscular Dystrophy 5(16%) Ataxia 3(10%) Mitochondrial Disorder 3(9%) Epilepsy 3(9%) Hereditary Small Vessel Disease 2(6%) Hereditary Neuropathy 2(6%) Neuromuscular Disorder 1(3%) Autism 1(3%) The most common presentations were neurodevelopmental delay (19%) and hyperkinetic movement disorders (19%), followed by muscular dystrophy (16%). Ataxia, epilepsy, and mitochondrial disorders each accounted for ~9–10%, while other phenotypes were less frequent. Table-8:Test Modality and Diagnostic Yield Test Type Cases (n) Diagnostic Yield (%) Repeat Expansion Analysis 5(15.6%) 66.7% Whole Exome/Clinical Exome 16(50%) 42.8% Gene Panels 7(21.8%) 48.9% Mitochondrial Gene Testing 3(9.37%) 0.0% Multiplex Ligation-dependent Probe Amplification(MLPA) 1(3.1%) 100.0% Repeat expansion analysis and gene panels showed higher diagnostic yields (≈67% and ≈49%, respectively) compared with whole/clinical exome sequencing (≈43%). MLPA, though used in one case, had 100% yield, while mitochondrial gene testing showed no diagnostic yield. Table 9: Phenotype-Based Diagnostic Performance Phenotype P/LP Detected Diagnostic Yield (%) Hereditary Small Vessel Disease 2/2 100.0 Hereditary Neuropathy 1/2 50.0 Muscular Dystrophy 3/6 50.0 Epilepsy Syndromes 2/3 66.7 Ataxia 2/3 66.7 Neurodevelopmental Delay/Autism 0/10 0.0 Mitochondrial Disorders 0/3 0.0 Movement Disorders 4/6 66.7 Highest yields were seen in hereditary small vessel disease (100%), epilepsy, ataxia, and movement disorders (≈67%), and muscular dystrophy (50%). Neurodevelopmental delay/autism and mitochondrial disorders had 0% yield. Table-10: Phenotypic Subgroups Phenotype Odds Ratio (OR) p-Value Interpretation Epilepsy 4.00 0.535 ↑ High diagnostic potential; sample size limits significance Ataxia 4.00 0.535 ↑ Similar to epilepsy; shows diagnostic promise Movement Disorders 3.80 0.190 ↑ High diagnostic trend; not statistically significant Muscular Dystrophy 2.00 0.638 ↑ Moderate yield Neuropathy 1.78 1.000 ↑ Suggestive trend; limited by sample size Neurodevelopmental Delay 0.00 0.003 ↓ Statistically significant low yield Epilepsy, ataxia, and movement disorders showed strong positive diagnostic trends, though limited by sample size. Neurodevelopmental delay had a statistically significant low diagnostic yield (p = 0.003).

This study provides a granular view of the diagnostic landscape of neurogenetic disorders in a South Indian tertiary care setting, focusing on pediatric and young adult populations. Although the cohort size was limited (n = 32), it captured the clinical and genetic heterogeneity of inherited neurological disorders and provided insights relevant to both diagnostic practice and genomic research in resource-constrained environments. The overall diagnostic yield in our cohort was 37%, which is slightly lower than some large-scale international studies but still comparable to several real-world cohorts. Our yield is close to that reported by Srivastava et al. (31%) [1]and Yang et al. (25–30%)[5], and somewhat lower than Monies et al[4]. (49%) and Helbig et al. (40–50%)[6]. Cohort-Specific Factors: The diagnostic yield in our study was significantly shaped by phenotype distribution, the type of genetic test employed, and the prevalence of variants of uncertain significance (VUS) Notably, a large subset of our cohort comprised individuals with neurodevelopmental disorders (NDD) and consanguineous backgrounds—both known to have elevated rates of uncertain or non-classifiable variants, which can lower definitive diagnostic yields. Role of Targeted Testing: Despite a moderate overall yield (37%), our data reinforces the principle that focused testing strategies, when driven by detailed phenotyping, are clinically effective and cost-efficient, especially in resource-constrained environments [2,5]. Gene panels yielded a diagnosis in 60% of selected cases, particularly effective in movement and epilepsy phenotypes [5,6]. Repeat expansion assays, applied to disorders such as ataxias and Huntington disease, achieved a 66.7% diagnostic rate, highlighting the benefit of aligning test modality with phenotype . In contrast, whole exome sequencing (WES)—while broader—had a lower yield of 42.8%, emphasizing the limitations of untargeted approaches in the absence of trio data or deep phenotyping . In our cohort of 32 patients, the overall diagnostic yield was 37%, comparable to other studies like Srivastava et al. (31%) [1] and Boone et al. (35–45%) [7], but lower than Monies et al. (49%) [4] and Seidahmed et al. (55%) [8], which had stronger consanguinity-driven recessive yields. Phenotype-guided testing showed significantly higher yields. Gene panels had the highest yield at 60%, especially in hyperkinetic movement disorders (60%), echoing findings from Boone et al. [7] and Helbig et al. [6]. Those studies also emphasized panel-based testing in movement and epilepsy phenotypes. Whole Exome Sequencing (WES) had a moderate yield at 35%, consistent with broader WES-based literature, including the studies by Yang et al. [5] and the DDD study.[9] Epilepsy phenotypes were particularly informative, with a yield of 66.6%, reinforcing the high detection rates noted in Helbig et al. [6] and Yang et al. [5]. The absence of pathogenic findings in neurodevelopmental disorders (NDD) and autism spectrum disorders (ASD) (0%) was statistically significant (p = 0.003). This reflects the challenge of diagnostic yield in these broad phenotypes unless detailed phenotyping or trio-WES is used, as seen Yang et al. [5]. Movement disorder/ataxia cases had a yield of 66%, bolstered by targeted gene panels and inclusion of repeat expansion assays, which had >60% success rates. These results align with Boone et al. [2] and underscore the role of specific phenotype testing. A strong consanguinity signal was present in 83% of diagnosed cases (OR = 5.0), comparable to findings in Monies et al. [4] and Seidahmed et al. [8]. This highlights the diagnostic utility of recessive inheritance models and homozygosity mapping in consanguineous populations [9,10]. Age Effects on Diagnostic Yield: Adults in our cohort had higher odds of receiving a genetic diagnosis (OR = 2.17). This may be attributed to presence of clearer phenotypes with age, and delayed onset of defining clinical features in several disorders. Conversely, early testing in infants often requires broader platforms like trio-WES, to compensate for evolving or non-specific phenotypes . This observation contrasts with findings from Yang et al., who reported higher diagnostic yields in neonates with well-defined syndromic features [5]. Consanguinity and Zygosity Consanguinity was noted in 6 of 32 patients (18.7%), and among those diagnosed, 83% harbored homozygous variants. A strong diagnostic association was observed (OR = 5.0), similar to reports from Monies et al. (76% autosomal recessive cases) [4] and Seidahmed et al. (55% overall yield in a consanguineous cohort) [4]. Homozygosity mapping remains a powerful adjunct in populations with high consanguinity rates. Variants of Uncertain Significance (VUS) a diagnostic Bottleneck VUS were identified in 47% of our cohort (15/32), predominantly in Neurodevelopmental disorders (NDD)/Autism spectrum disorders (ASD), and Consanguineous families. The majority of VUS were Homozygous, and located in genes of emerging or incompletely established clinical relevance. This is reflective of a global issue: the under-representation of South Asian alleles in major reference databases such as gnomAD and ClinVar, which hinders confident variant interpretation . VUS remain a growing challenge, especially in underrepresented populations like ours. To address this bottleneck, there is an urgent need for Parental segregation studies, Functional validation assays, Population-specific allele frequency databases, and Periodic reclassification as new evidence emerges.[11] Table-11: Comparative Diagnostic Yields in Neurogenetic Cohorts Study / Cohort Overall Yield Top Phenotype/Test Yields NDD/Autism Yield Epilepsy Yield Movement/Ataxia Yield Consanguinity Impact Our Study (n=32) 37% Panels: 60%, Hyperkinetic: 60%, WES: 35% 0% (p = 0.003) 66.6% 66% OR = 5.0, 83% homozygous Helbig et al.[6] 40–50% Epilepsy panels ~60% 10–30% 50–60% 55–65% Not emphasized Boone et al.[7] 35–45% Movement disorder panels ~20–25% 40–50% 45–55% Not reported Monies et al.[4] 49% Homozygosity + panels <30% 47% >50% 76% AR inheritance DDD Study[9] 27–40% Trio-WES 27–40% NA NA NA Yang et al.[5] 25–30% Trio > singleton WES 30–35% 45–50% ~40% NA Seidahmed et al.[8] 55% WES + consanguinity Low 42% >50% Strong AR effect Srivastava et al.[1] 31% Panels > WES in defined phenotypes ~30% 35–40% ~45% NA WES – Whole Exome Sequencing,OR – Odds Ratio,AR – Autosomal Recessive inheritance,NDD – Neurodevelopmental Disorders, DDD Study – Deciphering Developmental Disorders Study,Trio-WES – WES done on patient + both parents (trio),Singleton WES – WES done only on patient (no parental samples) Overall diagnostic yield (37%) is comparable to published neurogenetic studies. Consistent with other cohorts, phenotype-guided testing and autosomal recessive inheritance (especially with consanguinity) improved yield, while NDD/autism consistently showed lower diagnostic rates across studies. Low Diagnostic Yield in Neurodevelopmental Disorders The neurodevelopmental delay (NDD) and autism spectrum disorder (ASD) subgroup exhibited the lowest diagnostic yield in our cohort, a statistically significant finding (OR = 0.00, p = 0.003). This aligns with large-scale observations from the Deciphering Developmental Disorders (DDD) study[9] and subsequent reports by Yang et al. [11] and Srivastava et al. [6], which indicate that up to 40% of developmental disorders remain genetically unexplained despite high-throughput sequencing. Several mechanistic contributors are postulated, including: Non-coding region variants, Structural rearrangements, and Oligogenic or polygenic inheritance patterns—many of which are inadequately captured by conventional Whole Exome Sequencing (WES) [6,11]. This phenotype group also demonstrated a high burden of Variants of Uncertain Significance (VUS), particularly in consanguineous families, highlighting the critical role of family-based segregation analyses and functional assays for variant interpretation. Phenotypes with Higher Diagnostic Odds Certain clinical phenotypes, notably epilepsy, ataxia, and hyperkinetic movement disorders, demonstrated elevated odds of a positive genetic diagnosis (OR ~4.00). Although this trend did not reach statistical significance—likely due to limited sample sizes—it remains biologically and clinically relevant. These results are consistent with prior studies such as those by Boone et al. [3] and Helbig et al. [9], which report diagnostic yields exceeding 50% in epileptic encephalopathies and inherited movement disorders through panel-based and WES-based sequencing approaches. Future Directions Our findings emphasize the need for Refined variant interpretation frameworks, Multi-omics integration, including transcriptomics, methylomics, and long-read sequencing, to better resolve VUS-dense cohorts [6]. The importance of trio-based testing, periodic reanalysis, and phenotype-driven filtering algorithms has been repeatedly highlighted in recent literature [6,11], and should be incorporated into routine practice to enhance diagnostic yield. A regional allele frequency reference database containing phenotype-linked variant profiles from South Asian populations would likely improve both interpretability and cost-effectiveness, particularly for uncertain variants.

Gene panels and repeat expansion assays provided the highest diagnostic yields, especially in epilepsy and ataxia phenotypes, confirming their continued relevance in clinical settings. These disorders pose persistent challenges due to heterogeneous etiologies and technical limitations of current sequencing approaches.

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