Genomic Characterization of Pathogenic CNVs in a Familial Developmental Dyslexia Comorbid ADHD: A Case Study from Pakistan

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However, genomic evidence from South Asian populations remains limited, restricting insight into population-specific variants. Methodology: An integrated clinical, biochemical, and genomic investigation was conducted in a Pakistani family, focusing on a 9-year-old female proband diagnosed with comorbid DD and ADHD. Neuropsychological assessments evaluated reading ability, attention, and executive function. Biochemical profiling assessed neurotransmitter levels, vitamin D status, fatty acid composition, and oxidative stress markers. High-resolution Cytoscan HD microarray analysis was performed, followed by qPCR validation and familial segregation analysis. Results Neuropsychological testing revealed significant deficits in reading, attention, and executive functioning. Biochemical analysis demonstrated neurotransmitter dysregulation, vitamin D insufficiency, fatty acid imbalance, and elevated oxidative stress. Microarray analysis identified a heterozygous 258 kb de novo deletion at chromosome 17q12 (chr17:34.82–35.08 Mb; hg38), encompassing six genes involved in neurodevelopment and neuronal signaling. qPCR confirmed the deletion exclusively in the proband. Based on ACMG criteria, the copy number variant was classified as likely pathogenic. Discussion The findings suggest that rare structural variants may contribute to the comorbid presentation of DD and ADHD, potentially interacting with metabolic abnormalities to influence disease expression. Conclusion This study highlights the importance of CNV screening and integrative genomic approaches in underrepresented populations to improve understanding and diagnosis of neurodevelopmental disorders. Developmental Dyslexia (DD) Attention-Deficit/Hyperactivity Disorder (ADHD) Copy Number Variant (CNV) Genomic Analysis Neurodevelopmental Disorders Figures Figure 1 Figure 2 1. Introduction Developmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD) are among the most common neurodevelopmental conditions affecting children worldwide and pose significant challenges to learning and long-term functioning. DD is primarily characterized by persistent difficulties in reading accuracy, fluency, and comprehension that occur despite adequate intelligence and educational exposure. It affects approximately 5–10% of children globally (Peterson & Pennington, 2015 ). ADHD, marked by chronic inattention, hyperactivity, and impulsivity, has an estimated worldwide prevalence of 7.2% and frequently continues into adolescence and adulthood (Thomas et al., 2015 ). A substantial body of evidence indicates that DD and ADHD often co-occur. Epidemiological studies suggest that up to 40% of individuals with dyslexia also meet diagnostic criteria for ADHD (Willcutt et al., 2010 ), This frequent co morbidity points toward shared neurobiological pathways and overlapping genetic susceptibility rather than independent disease mechanisms. Genetic influences on both DD and ADHD have been demonstrated through twin studies, genome-wide association studies, and candidate gene analyses. In recent years, copy number variants (CNVs)structural genomic alterations involving deletions or duplications of DNA segments have gained attention as important contributors to neurodevelopmental disorders (Girirajan et al., 2011 ). CNVs can affect gene dosage, disrupt coding regions, or alter regulatory elements, thereby influencing genes involved in brain development, synaptic function, and cognitive processes (Marshall et al., 2008 ; Redin et al., 2014 ). Despite growing interest in CNVs, studies focusing on familial cases from underrepresented populations, particularly in South Asia, remain limited. The Pakistani population is genetically diverse and shaped by unique demographic and cultural factors, which may give rise to rare or population-specific variants that are not captured in existing reference datasets. Exploring CNVs in Pakistani families therefore offers valuable insight into the genetic architecture of neurodevelopmental disorders. In this study, we present a detailed genomic analysis of a Pakistani family with a 9-year-old female proband diagnosed with comorbid DD and ADHD. High-resolution Cytoscan HD microarray analysis was used to identify genome-wide CNVs, followed by systematic bioinformatics filtering to prioritize variants of potential clinical relevance. Candidate CNVs were validated using quantitative PCR, and familial segregation analysis was performed to distinguish de novo from inherited events. By integrating genomic data with clinical findings, this study aims to improve understanding of the genetic basis of DD and ADHD and highlights the importance of CNV analysis in familial neurodevelopmental research. 2. Materials and Methods 2.1 Study Design and Ethics This study investigated a single Pakistani family with a focus on a 9-year-old female proband diagnosed with both developmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD). Both parents were clinically unaffected, while extended family members included individuals with reported neurodevelopmental features. Clinical assessments were carried out at collaborating child neuropsychiatric clinics. Written informed consent was obtained from the legal guardians. Ethical approval was granted by the Institutional Review Board of COMSATS University Islamabad, Pakistan (CUI/BIO/ERB/2022/11), and the study was conducted in accordance with the Declaration of Helsinki. 2.2 Neuropsychological Assessment Standardized neuropsychological tests were used to confirm the diagnoses of DD and ADHD. Reading and language abilities were assessed using the Wechsler Individual Achievement Test (WIAT-III), Woodcock–Johnson IV Tests of Achievement, and the Comprehensive Test of Phonological Processing (CTOPP-2). ADHD symptoms and executive function were evaluated using the Conners’ Continuous Performance Test (CPT-3), Behavior Rating Inventory of Executive Function (BRIEF-2), and ADHD Rating Scale–5. Final diagnoses were based on DSM-5 criteria, test scores, and clinical judgment. 2.3 Clinical Biochemistry Blood samples were collected from the proband to examine biochemical markers related to neurodevelopmental disorders. Serum was separated using standard laboratory procedures. Key markers associated with metabolic function, oxidative stress, neurotransmitter balance, vitamin D levels, and essential fatty acids were measured using established methods, including enzyme-linked immunosorbent assays (ELISA), spectrophotometric techniques, and high-performance liquid chromatography (HPLC). 2.4 DNA Extraction and Microarray Analysis Peripheral blood samples (2 mL) were obtained from the proband and both parents and collected in EDTA tubes. Genomic DNA was extracted using the phenol–chloroform method and quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). Genome-wide copy number analysis was performed using the CytoScan HD microarray platform (Thermo Fisher Scientific, USA), which provides high-resolution coverage with more than 2.6 million probes. Data were analyzed using Chromosome Analysis Suite (ChAS) software version 4.2. Quality control checks were applied to all samples. Only samples meeting standard thresholds for SNP quality control (SNP-QC > 15) and median absolute pairwise difference (MAPD < 0.25) were included. Copy number variants (CNVs) were identified using log2 ratio thresholds, with deletions defined as regions ≥ 50 kb supported by at least 20 probes and duplications defined as regions ≥ 100 kb supported by at least 50 probes. 2.5 CNV Filtering and Interpretation Identified CNVs were mapped to the GRCh38/hg38 reference genome and compared with public databases, including OMIM, the Database of Genomic Variants (DGV), DECIPHER, and ClinVar. CNVs not detected in either parent were prioritized as de novo variants. Variants overlapping genes involved in brain development, cognitive function, or neuropsychiatric conditions were considered potentially relevant. Pathogenicity was evaluated according to American College of Medical Genetics and Genomics (ACMG) guidelines. 2.6 qPCR Validation Selected CNVs were validated using quantitative PCR (qPCR) on a Rotor-Gene Q real-time PCR system (QIAGEN, Germany) with SYBR Green chemistry. Primers targeting regions within the CNVs were designed using Primer3 software. Each reaction contained genomic DNA, SYBR Green master mix, and specific primers. Thermal cycling included an initial denaturation step followed by 40 amplification cycles. Relative copy number changes were calculated using the ΔΔCt method, with ACTB used as the reference gene. Fold changes greater than 2 or less than 0.5 were considered significant. Parental samples were tested to determine whether CNVs were inherited or occurred de novo, and only validated CNVs were reported. 3. Results 3.1 Clinical Biochemistry Findings The proband displayed several biochemical abnormalities linked to neurodevelopmental disorders. Serum markers were compared with age-matched controls. 3.2 Neurotransmitter Imbalance Serum dopamine and serotonin levels, both implicated in ADHD pathophysiology, were significantly reduced in the ADHD proband compared to controls (p < 0.05). Additionally, the serotonin-to-dopamine ratio was altered, suggesting dysregulated neurotransmitter signaling. In contrast, the dyslexia-only proband did not show significant changes, indicating a distinct biochemical profile between ADHD and DD. 3.3 Vitamin D Status The ADHD proband exhibited markedly low vitamin D levels (mean 15 ng/mL) compared to controls (mean 30 ng/mL; p < 0.01). No significant differences were observed in the dyslexia group. These findings suggest that vitamin D deficiency may be more closely associated with ADHD than with DD. 3.4 Fatty Acid Profile and Oxidative Stress Essential omega-3 fatty acids (EPA and DHA) were significantly lower in the ADHD proband (p < 0.05). Levels of malondialdehyde (MDA), a marker of oxidative stress, were elevated in ADHD (mean 5.2 µM) relative to both controls and the dyslexia group (mean 3.0 µM; p < 0.05), indicating increased oxidative stress and potential cellular dysfunction. 3.5 Metabolic Alterations Fasting glucose levels were higher in both ADHD and dyslexia probands (mean 110 mg/dL) compared to controls (mean 85 mg/dL; p < 0.01), suggesting altered glucose metabolism. In addition, ADHD probands had elevated total cholesterol and triglycerides, whereas the dyslexia group showed no significant lipid changes. Collectively, these results highlight that metabolic and oxidative imbalances may contribute to the cognitive and behavioral features of ADHD and to a lesser extent, DD. 3.6 Integration with Genomic Findings Given the biochemical abnormalities observed in the proband, we next examined potential genetic contributors. Chromosomal microarray analysis using the CytoScan HD platform identified a heterozygous 258 kb deletion on chromosome 17q12 (chr17:34,822,000–35,080,000; hg38), detected exclusively in the female proband. Quantitative PCR confirmed the deletion and verified its absence in both parents, indicating a de novo event. The deletion spans six genes (TBC1D3D, TBC1D3C, LOC101929950, TBC1D3E, TBC1D3, and NPEPPS), several of which play roles in neurodevelopmental pathways, including neuronal differentiation, synaptic signaling, and cortical expansion. Notably, TBC1D3 gene family members regulate Ras signaling and may influence neural progenitor proliferation, while NPEPPS is involved in protein degradation and cognitive processes. The co-occurrence of metabolic and neurotransmitter imbalances with a de novo pathogenic CNV suggests a combined contribution of biochemical and genetic factors to the clinical phenotype, providing a more integrated understanding of the proband’s DD and ADHD presentation. Table 1 Summary of CNVs identified in the proband and parental segregation analysis Serial Gene Cytoband Proband (CMA Call) Mother Father qPCR Relative Dosage* Interpretation 1 TBC1D3 17q12 Loss No CNV No CNV ~ 1.1 De novo CNV; absent in both parents. qPCR shows dosage near 1.0, suggesting normal copy number. Heterozygous deletion not confirmed. 2 NPEPPS 17q21.32 Loss No CNV No CNV ~ 1.3 Gene mapped to 17q21.32. qPCR does not support a heterozygous deletion. Table 2 Key genes within the 17q12 region and their known or hypothesized clinical relevance Gene Known / Hypothesized Role Associated Phenotypes (NDD / Other) Reference HNF1B Transcription factor critical for kidney, pancreas, and urogenital tract development Renal and urinary tract malformations, cystic kidneys; maturity-onset diabetes of the young (MODY5); occasionally neurodevelopmental or neuropsychiatric features when deletion includes 17q12 region Roehlen et al., 2018; review of 17q12 deletion syndrome phenotypes involving HNF1B LHX1 LIM homeobox transcription factor involved in neural and urogenital development Hypothesized contributor to neurodevelopmental and neuropsychiatric features (cognitive or behavioral issues) as part of 17q12 deletion; may also influence reproductive/urogenital anomalies Song et al., 2024; case report and review of 17q12 deletion syndrome noting LHX1 as candidate for neuropsychiatric features GGNBP2 Implicated in kidney and reproductive system development in deletion contexts Deletion may contribute to renal/reproductive anomalies; possible modifier of phenotypic variability in 17q12 deletion Song et al., 2024; describes deletion of HNF1B, LHX1, GGNBP2 in a patient with 17q12 deletion syndrome Multiple other 17q12 genes (e.g., CCL family, TBC1D family) Diverse roles including immune signaling and intracellular trafficking; many are copy-number variable in general population Generally no consistent phenotype; may contribute to variable expressivity; core phenotypes primarily linked to HNF1B, LHX1, and possibly GGNBP2 Genotype–phenotype reviews of 17q12 region; only a subset of genes strongly implicated in core clinical features 4. Discussion The biochemical profile of the proband highlights the potential role of metabolic and neurochemical disruptions in ADHD. Reduced serum dopamine and serotonin levels, along with an altered serotonin-to-dopamine ratio, indicate dysregulated neurotransmitter signaling, which is consistent with prior evidence linking dopamine deficiency to attentional deficits and hyperactivity (Arnsten, 2009; Aoki et al., 2018; Faraone et al., 2015). Vitamin D deficiency, observed predominantly in the ADHD proband, aligns with studies suggesting that low vitamin D may contribute to neurodevelopmental disturbances (Anglin et al., 2013). Elevated malondialdehyde (MDA) levels further suggest increased oxidative stress, supporting the notion of metabolic dysregulation contributing to neuronal vulnerability and cognitive impairment in ADHD (Koc et al., 2013). Collectively, these findings indicate that ADHD may involve complex interactions between neurotransmitter imbalance, metabolic dysfunction, and oxidative stress, which could serve as potential targets for therapeutic interventions. Complementing these biochemical findings, genomic analysis revealed a heterozygous 258 kb de novo deletion on chromosome 17q12, encompassing TBC1D3D, TBC1D3C, LOC101929950, TBC1D3E, TBC1D3, and NPEPPS. The TBC1D3 gene family is known to regulate Ras/MAPK signaling and neural progenitor proliferation, processes critical for cortical development and synaptic plasticity (Suzuki et al., 2010 ; Donohoe et al., 2021 ). Deletion of these genes may disrupt neuronal proliferation, migration, and cortical organization, potentially contributing to the cognitive and attentional deficits observed. Similarly, NPEPPS encodes a puromycin-sensitive aminopeptidase involved in protein degradation, neuroprotection, and synaptic function, with dysregulation linked to cognitive impairment and neurodegeneration (Osaka et al., 2004 ; Goto et al., 2009). The de novo origin of this CNV reinforces its clinical significance. According to ACMG guidelines, spontaneous CNVs affecting neurologically relevant genes and larger than 200 kb are likely pathogenic (Riggs et al., 2020 ). Population studies also show enrichment of de novo CNVs in neurodevelopmental and psychiatric disorders (Coe et al., 2014 ). Importantly, the combination of biochemical alterations and a pathogenic CNV suggests that ADHD and DD may result from converging metabolic and genetic mechanisms. This case further illustrates the shared genetic architecture between DD and ADHD. Prior studies indicate that both common and rare variants can influence reading ability and attention, supporting the “generalist genes” hypothesis (Gialluisi et al., 2020 ; Willcutt et al., 2010 ). Structural variants, such as the 17q12 deletion, may perturb overlapping neurodevelopmental pathways, including synaptic signaling, cortical development, and transcriptional regulation, leading to comorbid phenotypes. Finally, this study highlights the value of integrating high-resolution chromosomal microarray with functional validation techniques like qPCR in familial contexts. Such approaches are especially important in underrepresented populations, where rare or private variants may not be captured in global databases. Pedigree analysis suggests a potential multigenic or modifying variant influence, which could be further explored through whole-genome sequencing to identify additional regulatory or interacting variants contributing to phenotype expression. The convergence of biochemical dysregulation and a pathogenic CNV in this proband provides actionable insights for potential interventions. Neurotransmitter imbalances suggest that pharmacological strategies targeting dopaminergic and serotonergic pathways may help alleviate attentional and behavioral symptoms in ADHD. Vitamin D supplementation and dietary interventions to correct omega-3 fatty acid deficiency may support neurodevelopment and reduce oxidative stress. Furthermore, the identification of a de novo 17q12 deletion underscores the importance of genetic counseling and personalized monitoring, particularly in families with a history of neurodevelopmental disorders. Integrating metabolic and genetic profiling into clinical practice may enable targeted, multifaceted therapeutic strategies that address both biochemical and genomic contributors to DD and ADHD. 5. Conclusion This study identifies a novel de novo 17q12 deletion in a female proband with comorbid developmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD), encompassing neurodevelopmentally relevant genes including TBC1D3 and NPEPPS. Validation by qPCR confirms its de novo origin and supports a likely pathogenic role in the proband’s cognitive and behavioral phenotype. The integration of genomic, biochemical, neuropsychological, and familial data underscores the value of a multifaceted approach for uncovering rare structural variants in complex neurodevelopmental disorders. These findings highlight the importance of CNV screening in underrepresented populations and provide a foundation for personalized diagnostics and targeted interventions. Declarations Future Prospects Future research should aim to further elucidate the functional and population-level impact of such CNVs: Functional Characterization Use neuronal models or brain organoids to investigate the role of deleted genes in cortical development, synaptic regulation, and neuronal differentiation. Population Genomics Examine the prevalence and phenotypic consequences of 17q12 CNVs in large South Asian cohorts to identify population-specific variants and refine genotype–phenotype correlations. Transcriptomic Profiling Apply RNA sequencing to map downstream regulatory networks disrupted by gene loss, focusing on pathways relevant to DD and ADHD. Epigenetic Analysis Explore CNV interactions with DNA methylation, chromatin remodeling, and other epigenetic modifications during critical developmental windows. Familial Whole-Genome Sequencing Investigate multigenerational pedigrees to identify potential modifier variants or epistatic interactions affecting penetrance and phenotypic expression. By combining functional, transcriptomic, epigenetic, and population-level approaches, future studies can provide a mechanistic understanding of CNV-driven neurodevelopmental disorders, advancing precision diagnostics, risk assessment, and targeted therapeutic strategies. Conflict of Interest Statement Authors declare that there are no commercial or financial relationships that could be construed as a potential conflict of interest in relation to this study. Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution S.H and M.S wrote the main manuscript text, and F.K. prepared the figures and results interpretation References Coe BP et al (2014) Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet 46(10):1063–1071 Donohoe ME et al (2021) TBC1D3: A human-specific gene shaping brain development. Trends Neurosci 44(9):708–722 Gialluisi A et al (2020) Genome-wide association study of reading traits reveals shared genetic architecture with neuropsychiatric disorders. Nat Commun 11:2170 Girirajan S, Brkanac Z, Coe BP, Baker C, Vives L, Vu TH, Eichler EE (2011) Relative burden of large CNVs on a range of neurodevelopmental phenotypes. PLoS Genet 7(11):e1002334. https://doi.org/10.1371/journal.pgen.1002334 Marshall CR, Noor A, Vincent JB, Lionel AC, Feuk L, Skaug J, Scherer SW (2008) Structural variation of chromosomes in autism spectrum disorder. 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Psychol Bull 126(6):735–761 Willcutt EG, Pennington BF, DeFries JC (2010) Etiology of comorbidity between reading disability and attention-deficit/hyperactivity disorder: The case for generalist genes. Psychol Bull 126(6):735–761. https://doi.org/10.1037/0033-2909.126.6.735 Zufferey F et al (2012) 17q12 deletions in neurodevelopmental disorders. Eur J Hum Genet 20(6):598–606 Additional Declarations No competing interests reported. Supplementary Files floatimage1.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8483680","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568270298,"identity":"346de6cb-f001-43b0-9cbf-893383a7646d","order_by":0,"name":"Shujjah 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17:18:05","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67935,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8483680/v1/bf9c3186d8e97427b102a96b.html"},{"id":99636848,"identity":"6d21e600-07d3-435b-9f79-fe646f3a067c","added_by":"auto","created_at":"2026-01-06 17:18:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePedigree of the familial case showing the proband (III-3) with both ADHD (blue) and DD (red), as well as affected relatives with DD (red) or ADHD (blue) across two generations.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8483680/v1/93a8f5dd6d28234eba955718.png"},{"id":99794405,"identity":"353af2fe-1763-424e-a4aa-2fc80cf73cd2","added_by":"auto","created_at":"2026-01-08 13:34:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCNV classification workflow applied in this case report,CNVs (200 kb–5 Mb) were evaluated using sequential filters integrating database evidence, phenotype correlation, family segregation, and gene overlap to support pathogenic, likely pathogenic, or VUS classification.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8483680/v1/387389322925ad920104dee4.png"},{"id":101760231,"identity":"6833f946-f80a-4ca1-934b-dfadd090c777","added_by":"auto","created_at":"2026-02-03 11:13:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1283139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8483680/v1/c206014f-4e28-4589-9cc4-a0800f910951.pdf"},{"id":99795039,"identity":"328e0110-5ecb-492d-8311-f22e319b4799","added_by":"auto","created_at":"2026-01-08 13:36:52","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1180458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8483680/v1/916969727fa11384bab3e830.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenomic Characterization of Pathogenic CNVs in a Familial Developmental Dyslexia Comorbid ADHD: A Case Study from Pakistan\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDevelopmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD) are among the most common neurodevelopmental conditions affecting children worldwide and pose significant challenges to learning and long-term functioning. DD is primarily characterized by persistent difficulties in reading accuracy, fluency, and comprehension that occur despite adequate intelligence and educational exposure. It affects approximately 5\u0026ndash;10% of children globally (Peterson \u0026amp; Pennington, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). ADHD, marked by chronic inattention, hyperactivity, and impulsivity, has an estimated worldwide prevalence of 7.2% and frequently continues into adolescence and adulthood (Thomas et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA substantial body of evidence indicates that DD and ADHD often co-occur. Epidemiological studies suggest that up to 40% of individuals with dyslexia also meet diagnostic criteria for ADHD (Willcutt et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), This frequent co morbidity points toward shared neurobiological pathways and overlapping genetic susceptibility rather than independent disease mechanisms.\u003c/p\u003e \u003cp\u003eGenetic influences on both DD and ADHD have been demonstrated through twin studies, genome-wide association studies, and candidate gene analyses. In recent years, copy number variants (CNVs)structural genomic alterations involving deletions or duplications of DNA segments have gained attention as important contributors to neurodevelopmental disorders (Girirajan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CNVs can affect gene dosage, disrupt coding regions, or alter regulatory elements, thereby influencing genes involved in brain development, synaptic function, and cognitive processes (Marshall et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Redin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing interest in CNVs, studies focusing on familial cases from underrepresented populations, particularly in South Asia, remain limited. The Pakistani population is genetically diverse and shaped by unique demographic and cultural factors, which may give rise to rare or population-specific variants that are not captured in existing reference datasets. Exploring CNVs in Pakistani families therefore offers valuable insight into the genetic architecture of neurodevelopmental disorders.\u003c/p\u003e \u003cp\u003eIn this study, we present a detailed genomic analysis of a Pakistani family with a 9-year-old female proband diagnosed with comorbid DD and ADHD. High-resolution Cytoscan HD microarray analysis was used to identify genome-wide CNVs, followed by systematic bioinformatics filtering to prioritize variants of potential clinical relevance. Candidate CNVs were validated using quantitative PCR, and familial segregation analysis was performed to distinguish de novo from inherited events. By integrating genomic data with clinical findings, this study aims to improve understanding of the genetic basis of DD and ADHD and highlights the importance of CNV analysis in familial neurodevelopmental research.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eStudy Design and Ethics\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study investigated a single Pakistani family with a focus on a 9-year-old female proband diagnosed with both developmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD). Both parents were clinically unaffected, while extended family members included individuals with reported neurodevelopmental features. Clinical assessments were carried out at collaborating child neuropsychiatric clinics. Written informed consent was obtained from the legal guardians. Ethical approval was granted by the Institutional Review Board of COMSATS University Islamabad, Pakistan (CUI/BIO/ERB/2022/11), and the study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eNeuropsychological Assessment\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eStandardized neuropsychological tests were used to confirm the diagnoses of DD and ADHD. Reading and language abilities were assessed using the Wechsler Individual Achievement Test (WIAT-III), Woodcock\u0026ndash;Johnson IV Tests of Achievement, and the Comprehensive Test of Phonological Processing (CTOPP-2). ADHD symptoms and executive function were evaluated using the Conners\u0026rsquo; Continuous Performance Test (CPT-3), Behavior Rating Inventory of Executive Function (BRIEF-2), and ADHD Rating Scale\u0026ndash;5. Final diagnoses were based on DSM-5 criteria, test scores, and clinical judgment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cem\u003eClinical Biochemistry\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBlood samples were collected from the proband to examine biochemical markers related to neurodevelopmental disorders. Serum was separated using standard laboratory procedures. Key markers associated with metabolic function, oxidative stress, neurotransmitter balance, vitamin D levels, and essential fatty acids were measured using established methods, including enzyme-linked immunosorbent assays (ELISA), spectrophotometric techniques, and high-performance liquid chromatography (HPLC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 \u003cem\u003eDNA Extraction and Microarray Analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003ePeripheral blood samples (2 mL) were obtained from the proband and both parents and collected in EDTA tubes. Genomic DNA was extracted using the phenol\u0026ndash;chloroform method and quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). Genome-wide copy number analysis was performed using the CytoScan HD microarray platform (Thermo Fisher Scientific, USA), which provides high-resolution coverage with more than 2.6\u0026nbsp;million probes. Data were analyzed using Chromosome Analysis Suite (ChAS) software version 4.2.\u003c/p\u003e \u003cp\u003eQuality control checks were applied to all samples. Only samples meeting standard thresholds for SNP quality control (SNP-QC\u0026thinsp;\u0026gt;\u0026thinsp;15) and median absolute pairwise difference (MAPD\u0026thinsp;\u0026lt;\u0026thinsp;0.25) were included. Copy number variants (CNVs) were identified using log2 ratio thresholds, with deletions defined as regions\u0026thinsp;\u0026ge;\u0026thinsp;50 kb supported by at least 20 probes and duplications defined as regions\u0026thinsp;\u0026ge;\u0026thinsp;100 kb supported by at least 50 probes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 \u003cem\u003eCNV Filtering and Interpretation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIdentified CNVs were mapped to the GRCh38/hg38 reference genome and compared with public databases, including OMIM, the Database of Genomic Variants (DGV), DECIPHER, and ClinVar. CNVs not detected in either parent were prioritized as de novo variants. Variants overlapping genes involved in brain development, cognitive function, or neuropsychiatric conditions were considered potentially relevant. Pathogenicity was evaluated according to American College of Medical Genetics and Genomics (ACMG) guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 \u003cem\u003eqPCR Validation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSelected CNVs were validated using quantitative PCR (qPCR) on a Rotor-Gene Q real-time PCR system (QIAGEN, Germany) with SYBR Green chemistry. Primers targeting regions within the CNVs were designed using Primer3 software. Each reaction contained genomic DNA, SYBR Green master mix, and specific primers. Thermal cycling included an initial denaturation step followed by 40 amplification cycles. Relative copy number changes were calculated using the ΔΔCt method, with ACTB used as the reference gene. Fold changes greater than 2 or less than 0.5 were considered significant. Parental samples were tested to determine whether CNVs were inherited or occurred de novo, and only validated CNVs were reported.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eClinical Biochemistry Findings\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe proband displayed several biochemical abnormalities linked to neurodevelopmental disorders. Serum markers were compared with age-matched controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cem\u003eNeurotransmitter Imbalance\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSerum dopamine and serotonin levels, both implicated in ADHD pathophysiology, were significantly reduced in the ADHD proband compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the serotonin-to-dopamine ratio was altered, suggesting dysregulated neurotransmitter signaling. In contrast, the dyslexia-only proband did not show significant changes, indicating a distinct biochemical profile between ADHD and DD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eVitamin D Status\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe ADHD proband exhibited markedly low vitamin D levels (mean 15 ng/mL) compared to controls (mean 30 ng/mL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No significant differences were observed in the dyslexia group. These findings suggest that vitamin D deficiency may be more closely associated with ADHD than with DD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cem\u003eFatty Acid Profile and Oxidative Stress\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eEssential omega-3 fatty acids (EPA and DHA) were significantly lower in the ADHD proband (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Levels of malondialdehyde (MDA), a marker of oxidative stress, were elevated in ADHD (mean 5.2 \u0026micro;M) relative to both controls and the dyslexia group (mean 3.0 \u0026micro;M; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating increased oxidative stress and potential cellular dysfunction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cem\u003eMetabolic Alterations\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFasting glucose levels were higher in both ADHD and dyslexia probands (mean 110 mg/dL) compared to controls (mean 85 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting altered glucose metabolism. In addition, ADHD probands had elevated total cholesterol and triglycerides, whereas the dyslexia group showed no significant lipid changes. Collectively, these results highlight that metabolic and oxidative imbalances may contribute to the cognitive and behavioral features of ADHD and to a lesser extent, DD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 \u003cem\u003eIntegration with Genomic Findings\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eGiven the biochemical abnormalities observed in the proband, we next examined potential genetic contributors. Chromosomal microarray analysis using the CytoScan HD platform identified a heterozygous 258 kb deletion on chromosome 17q12 (chr17:34,822,000\u0026ndash;35,080,000; hg38), detected exclusively in the female proband. Quantitative PCR confirmed the deletion and verified its absence in both parents, indicating a de novo event.\u003c/p\u003e \u003cp\u003eThe deletion spans six genes (TBC1D3D, TBC1D3C, LOC101929950, TBC1D3E, TBC1D3, and NPEPPS), several of which play roles in neurodevelopmental pathways, including neuronal differentiation, synaptic signaling, and cortical expansion. Notably, TBC1D3 gene family members regulate Ras signaling and may influence neural progenitor proliferation, while NPEPPS is involved in protein degradation and cognitive processes.\u003c/p\u003e \u003cp\u003eThe co-occurrence of metabolic and neurotransmitter imbalances with a de novo pathogenic CNV suggests a combined contribution of biochemical and genetic factors to the clinical phenotype, providing a more integrated understanding of the proband\u0026rsquo;s DD and ADHD presentation.\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\u003eSummary of CNVs identified in the proband and parental segregation analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial\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\u003eCytoband\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProband (CMA Call)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMother\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFather\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eqPCR Relative Dosage*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBC1D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo CNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo CNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDe novo CNV; absent in both parents. qPCR shows dosage near 1.0, suggesting normal copy number. Heterozygous deletion not confirmed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPEPPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17q21.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo CNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo CNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGene mapped to 17q21.32. qPCR does not support a heterozygous deletion.\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 \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\u003eKey genes within the 17q12 region and their known or hypothesized clinical relevance\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=\"left\" 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\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnown / Hypothesized Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssociated Phenotypes (NDD / Other)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHNF1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranscription factor critical for kidney, pancreas, and urogenital tract development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenal and urinary tract malformations, cystic kidneys; maturity-onset diabetes of the young (MODY5); occasionally neurodevelopmental or neuropsychiatric features when deletion includes 17q12 region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoehlen et al., 2018; review of 17q12 deletion syndrome phenotypes involving HNF1B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIM homeobox transcription factor involved in neural and urogenital development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypothesized contributor to neurodevelopmental and neuropsychiatric features (cognitive or behavioral issues) as part of 17q12 deletion; may also influence reproductive/urogenital anomalies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSong et al., 2024; case report and review of 17q12 deletion syndrome noting LHX1 as candidate for neuropsychiatric features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGNBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImplicated in kidney and reproductive system development in deletion contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeletion may contribute to renal/reproductive anomalies; possible modifier of phenotypic variability in 17q12 deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSong et al., 2024; describes deletion of HNF1B, LHX1, GGNBP2 in a patient with 17q12 deletion syndrome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple other 17q12 genes (e.g., CCL family, TBC1D family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiverse roles including immune signaling and intracellular trafficking; many are copy-number variable in general population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenerally no consistent phenotype; may contribute to variable expressivity; core phenotypes primarily linked to HNF1B, LHX1, and possibly GGNBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenotype\u0026ndash;phenotype reviews of 17q12 region; only a subset of genes strongly implicated in core clinical features\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"},{"header":"4. Discussion","content":"\u003cp\u003eThe biochemical profile of the proband highlights the potential role of metabolic and neurochemical disruptions in ADHD. Reduced serum dopamine and serotonin levels, along with an altered serotonin-to-dopamine ratio, indicate dysregulated neurotransmitter signaling, which is consistent with prior evidence linking dopamine deficiency to attentional deficits and hyperactivity (Arnsten, 2009; Aoki et al., 2018; Faraone et al., 2015). Vitamin D deficiency, observed predominantly in the ADHD proband, aligns with studies suggesting that low vitamin D may contribute to neurodevelopmental disturbances (Anglin et al., 2013). Elevated malondialdehyde (MDA) levels further suggest increased oxidative stress, supporting the notion of metabolic dysregulation contributing to neuronal vulnerability and cognitive impairment in ADHD (Koc et al., 2013). Collectively, these findings indicate that ADHD may involve complex interactions between neurotransmitter imbalance, metabolic dysfunction, and oxidative stress, which could serve as potential targets for therapeutic interventions.\u003c/p\u003e \u003cp\u003eComplementing these biochemical findings, genomic analysis revealed a heterozygous 258 kb de novo deletion on chromosome 17q12, encompassing TBC1D3D, TBC1D3C, LOC101929950, TBC1D3E, TBC1D3, and NPEPPS. The TBC1D3 gene family is known to regulate Ras/MAPK signaling and neural progenitor proliferation, processes critical for cortical development and synaptic plasticity (Suzuki et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Donohoe et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Deletion of these genes may disrupt neuronal proliferation, migration, and cortical organization, potentially contributing to the cognitive and attentional deficits observed. Similarly, NPEPPS encodes a puromycin-sensitive aminopeptidase involved in protein degradation, neuroprotection, and synaptic function, with dysregulation linked to cognitive impairment and neurodegeneration (Osaka et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Goto et al., 2009).\u003c/p\u003e \u003cp\u003eThe de novo origin of this CNV reinforces its clinical significance. According to ACMG guidelines, spontaneous CNVs affecting neurologically relevant genes and larger than 200 kb are likely pathogenic (Riggs et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Population studies also show enrichment of de novo CNVs in neurodevelopmental and psychiatric disorders (Coe et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Importantly, the combination of biochemical alterations and a pathogenic CNV suggests that ADHD and DD may result from converging metabolic and genetic mechanisms.\u003c/p\u003e \u003cp\u003eThis case further illustrates the shared genetic architecture between DD and ADHD. Prior studies indicate that both common and rare variants can influence reading ability and attention, supporting the \u0026ldquo;generalist genes\u0026rdquo; hypothesis (Gialluisi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Willcutt et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Structural variants, such as the 17q12 deletion, may perturb overlapping neurodevelopmental pathways, including synaptic signaling, cortical development, and transcriptional regulation, leading to comorbid phenotypes.\u003c/p\u003e \u003cp\u003eFinally, this study highlights the value of integrating high-resolution chromosomal microarray with functional validation techniques like qPCR in familial contexts. Such approaches are especially important in underrepresented populations, where rare or private variants may not be captured in global databases. Pedigree analysis suggests a potential multigenic or modifying variant influence, which could be further explored through whole-genome sequencing to identify additional regulatory or interacting variants contributing to phenotype expression.\u003c/p\u003e \u003cp\u003eThe convergence of biochemical dysregulation and a pathogenic CNV in this proband provides actionable insights for potential interventions. Neurotransmitter imbalances suggest that pharmacological strategies targeting dopaminergic and serotonergic pathways may help alleviate attentional and behavioral symptoms in ADHD. Vitamin D supplementation and dietary interventions to correct omega-3 fatty acid deficiency may support neurodevelopment and reduce oxidative stress. Furthermore, the identification of a de novo 17q12 deletion underscores the importance of genetic counseling and personalized monitoring, particularly in families with a history of neurodevelopmental disorders. Integrating metabolic and genetic profiling into clinical practice may enable targeted, multifaceted therapeutic strategies that address both biochemical and genomic contributors to DD and ADHD.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identifies a novel de novo 17q12 deletion in a female proband with comorbid developmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD), encompassing neurodevelopmentally relevant genes including TBC1D3 and NPEPPS. Validation by qPCR confirms its de novo origin and supports a likely pathogenic role in the proband\u0026rsquo;s cognitive and behavioral phenotype. The integration of genomic, biochemical, neuropsychological, and familial data underscores the value of a multifaceted approach for uncovering rare structural variants in complex neurodevelopmental disorders. These findings highlight the importance of CNV screening in underrepresented populations and provide a foundation for personalized diagnostics and targeted interventions.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eFuture Prospects\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFuture research should aim to further elucidate the functional and population-level impact of such CNVs:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFunctional Characterization\u003c/strong\u003e \u003cp\u003eUse neuronal models or brain organoids to investigate the role of deleted genes in cortical development, synaptic regulation, and neuronal differentiation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePopulation Genomics\u003c/strong\u003e \u003cp\u003eExamine the prevalence and phenotypic consequences of 17q12 CNVs in large South Asian cohorts to identify population-specific variants and refine genotype\u0026ndash;phenotype correlations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTranscriptomic Profiling\u003c/strong\u003e \u003cp\u003eApply RNA sequencing to map downstream regulatory networks disrupted by gene loss, focusing on pathways relevant to DD and ADHD.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEpigenetic Analysis\u003c/strong\u003e \u003cp\u003eExplore CNV interactions with DNA methylation, chromatin remodeling, and other epigenetic modifications during critical developmental windows.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFamilial Whole-Genome Sequencing\u003c/strong\u003e \u003cp\u003eInvestigate multigenerational pedigrees to identify potential modifier variants or epistatic interactions affecting penetrance and phenotypic expression.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBy combining functional, transcriptomic, epigenetic, and population-level approaches, future studies can provide a mechanistic understanding of CNV-driven neurodevelopmental disorders, advancing precision diagnostics, risk assessment, and targeted therapeutic strategies.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e \u003cp\u003eAuthors declare that there are no commercial or financial relationships that could be construed as a potential conflict of interest in relation to this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding Statement\u003c/b\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.H and M.S wrote the main manuscript text, and F.K. prepared the figures and results interpretation\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCoe BP et al (2014) Refining analyses of copy number variation identifies specific genes associated with developmental delay. 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Psychol Bull 126(6):735\u0026ndash;761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.126.6.735\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.126.6.735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZufferey F et al (2012) 17q12 deletions in neurodevelopmental disorders. Eur J Hum Genet 20(6):598\u0026ndash;606\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Developmental Dyslexia (DD), Attention-Deficit/Hyperactivity Disorder (ADHD), Copy Number Variant (CNV), Genomic Analysis, Neurodevelopmental Disorders","lastPublishedDoi":"10.21203/rs.3.rs-8483680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8483680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDevelopmental dyslexia (DD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur and are believed to share genetic risk factors. However, genomic evidence from South Asian populations remains limited, restricting insight into population-specific variants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn integrated clinical, biochemical, and genomic investigation was conducted in a Pakistani family, focusing on a 9-year-old female proband diagnosed with comorbid DD and ADHD. Neuropsychological assessments evaluated reading ability, attention, and executive function. Biochemical profiling assessed neurotransmitter levels, vitamin D status, fatty acid composition, and oxidative stress markers. High-resolution Cytoscan HD microarray analysis was performed, followed by qPCR validation and familial segregation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuropsychological testing revealed significant deficits in reading, attention, and executive functioning. Biochemical analysis demonstrated neurotransmitter dysregulation, vitamin D insufficiency, fatty acid imbalance, and elevated oxidative stress. Microarray analysis identified a heterozygous 258 kb de novo deletion at chromosome 17q12 (chr17:34.82–35.08 Mb; hg38), encompassing six genes involved in neurodevelopment and neuronal signaling. qPCR confirmed the deletion exclusively in the proband. Based on ACMG criteria, the copy number variant was classified as likely pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings suggest that rare structural variants may contribute to the comorbid presentation of DD and ADHD, potentially interacting with metabolic abnormalities to influence disease expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the importance of CNV screening and integrative genomic approaches in underrepresented populations to improve understanding and diagnosis of neurodevelopmental disorders.\u003c/p\u003e","manuscriptTitle":"Genomic Characterization of Pathogenic CNVs in a Familial Developmental Dyslexia Comorbid ADHD: A Case Study from Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 17:18:00","doi":"10.21203/rs.3.rs-8483680/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a5649b6-eab1-4ffd-91f7-1d0ff1c041cd","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T10:54:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 17:18:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8483680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8483680","identity":"rs-8483680","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}